{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QA_djFCxPocg"
      },
      "source": [
        "# Information Extraction with Haystack and NuExtract\n",
        "\n",
        "*Authored by: [Stefano Fiorucci](https://github.com/anakin87)*\n",
        "\n",
        "In this notebook, we will see how to automate Information Extraction from textual data using Language Models.\n",
        "\n",
        "🎯 Goal: create an application to extract specific information from a given text or URL, following a user-defined structure.\n",
        "\n",
        "🧰 **Stack**\n",
        "- [Haystack 🏗️](https://haystack.deepset.ai?utm_campaign=developer-relations&utm_source=hf-cookbook): a customizable orchestration framework for building LLM applications. We will use Haystack to build the Information Extraction Pipeline.\n",
        "\n",
        "- [NuExtract](https://huggingface.co/numind/NuExtract): a small Language Model, specifically fine-tuned for structured data extraction."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Lzbl1yAZRUH"
      },
      "source": [
        "## Install dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-07-05T17:01:12.299629Z",
          "iopub.status.busy": "2024-07-05T17:01:12.299255Z",
          "iopub.status.idle": "2024-07-05T17:01:28.511696Z",
          "shell.execute_reply": "2024-07-05T17:01:28.510490Z",
          "shell.execute_reply.started": "2024-07-05T17:01:12.299594Z"
        },
        "id": "zwDxct7PPUgZ",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "! pip install haystack-ai trafilatura transformers pyvis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6eWvzas4iFW2"
      },
      "source": [
        "## Components\n",
        "\n",
        "Haystack has two main concepts: [Components and Pipelines](https://docs.haystack.deepset.ai/docs/components_overview?utm_campaign=developer-relations&utm_source=hf-cookbook).\n",
        "\n",
        "🧩 **Components** are building blocks that perform a single task: file conversion, text generation, embedding creation...\n",
        "\n",
        "➿ **Pipelines** allow you to define the flow of data through your LLM application, by combining Components in a directed (cyclic) graph.\n",
        "\n",
        "*We will now introduce the various components of our Information Extraction application. Afterwards, we will integrate them into a Pipeline.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cetMTw5V-nDG"
      },
      "source": [
        "### `LinkContentFetcher` and `HTMLToDocument`: extract text from web pages\n",
        "\n",
        "In our experiment, we will extract data from startup funding announcements found on the web.\n",
        "\n",
        "To download web pages and extract text, we use two components:\n",
        "- [`LinkContentFetcher`](https://docs.haystack.deepset.ai/docs/linkcontentfetcher?utm_campaign=developer-relations&utm_source=hf-cookbook): fetches the content of some URLs and returns a list of content streams (as [`ByteStream` objects](https://docs.haystack.deepset.ai/docs/data-classes#bytestream?utm_campaign=developer-relations&utm_source=hf-cookbook)).\n",
        "- [`HTMLToDocument`](https://docs.haystack.deepset.ai/docs/htmltodocument?utm_campaign=developer-relations&utm_source=hf-cookbook): converts HTML sources into textual [`Documents`](https://docs.haystack.deepset.ai/docs/data-classes#document?utm_campaign=developer-relations&utm_source=hf-cookbook)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2024-07-05T17:01:28.515270Z",
          "iopub.status.busy": "2024-07-05T17:01:28.514606Z",
          "iopub.status.idle": "2024-07-05T17:01:34.711768Z",
          "shell.execute_reply": "2024-07-05T17:01:34.710676Z",
          "shell.execute_reply.started": "2024-07-05T17:01:28.515224Z"
        },
        "id": "tRO67BDoqdLq",
        "outputId": "8c9a3f0a-d3c8-4beb-ee6a-016717431bb3",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'documents': [Document(id=65bb1ce4b6db2f154d3acfa145fa03363ef93f751fb8599dcec3aaf75aa325b9, content: 'This domain is for use in illustrative examples in documents. You may use this domain in literature ...', meta: {'content_type': 'text/html', 'url': 'https://example.com/'})]}\n"
          ]
        }
      ],
      "source": [
        "from haystack.components.fetchers import LinkContentFetcher\n",
        "from haystack.components.converters import HTMLToDocument\n",
        "\n",
        "\n",
        "fetcher = LinkContentFetcher()\n",
        "\n",
        "streams = fetcher.run(urls=[\"https://example.com/\"])[\"streams\"]\n",
        "\n",
        "converter = HTMLToDocument()\n",
        "docs = converter.run(sources=streams)\n",
        "\n",
        "print(docs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5qyEBSDJEO1w"
      },
      "source": [
        "### `HuggingFaceLocalGenerator`: load and try the model\n",
        "\n",
        "We use the [`HuggingFaceLocalGenerator`](https://docs.haystack.deepset.ai/docs/huggingfacelocalgenerator?utm_campaign=developer-relations&utm_source=hf-cookbook), a text generation component that allows loading a model hosted on Hugging Face using the Transformers library.\n",
        "\n",
        "Haystack supports many other [Generators](https://docs.haystack.deepset.ai/docs/generators?utm_campaign=developer-relations&utm_source=hf-cookbook), including [`HuggingFaceAPIGenerator`](https://docs.haystack.deepset.ai/docs/huggingfaceapigenerator?utm_campaign=developer-relations&utm_source=hf-cookbook) (compatible with Hugging Face APIs and TGI).\n",
        "\n",
        "We load [NuExtract](https://huggingface.co/numind/NuExtract), a model fine-tuned from `microsoft/Phi-3-mini-4k-instruct` to perform structured data extraction from text. The model size is 3.8B parameters. Other variants are also available: `NuExtract-tiny` (0.5B) and `NuExtract-large` (7B).\n",
        "\n",
        "The model is loaded with `bfloat16` precision to fit in Colab with negligible performance loss compared to FP32, as suggested in the model card.\n",
        "\n",
        "#### Notes on Flash Attention\n",
        "\n",
        "At inference time, you will probably see a warning saying: \"You are not running the flash-attention implementation\".\n",
        "\n",
        "GPUs available on free environments like Colab or Kaggle do not support it, so we decided to not use it in this notebook.\n",
        "\n",
        "In case your GPU architecture supports it ([details](https://github.com/Dao-AILab/flash-attention)), you can install it and get a speed-up as follows:\n",
        "```bash\n",
        "pip install flash-attn --no-build-isolation\n",
        "```\n",
        "\n",
        "Then add `\"attn_implementation\": \"flash_attention_2\"` to `model_kwargs`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-07-05T17:01:34.713833Z",
          "iopub.status.busy": "2024-07-05T17:01:34.713339Z",
          "iopub.status.idle": "2024-07-05T17:02:54.005033Z",
          "shell.execute_reply": "2024-07-05T17:02:54.003847Z",
          "shell.execute_reply.started": "2024-07-05T17:01:34.713801Z"
        },
        "id": "mUA8M0eed6Bo",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from haystack.components.generators import HuggingFaceLocalGenerator\n",
        "import torch\n",
        "\n",
        "generator = HuggingFaceLocalGenerator(model=\"numind/NuExtract\",\n",
        "                                      huggingface_pipeline_kwargs={\"model_kwargs\": {\"torch_dtype\":torch.bfloat16}})\n",
        "\n",
        "# effectively load the model (warm_up is automatically invoked when the generator is part of a Pipeline)\n",
        "generator.warm_up()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2SeDKME6N6Y7"
      },
      "source": [
        "The model supports a specific prompt structure, as can be inferred from the model card.\n",
        "\n",
        "Let's manually create a prompt to try the model. Later, we will see how to dynamically create the prompt based on different inputs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2024-07-05T17:02:54.007205Z",
          "iopub.status.busy": "2024-07-05T17:02:54.006458Z",
          "iopub.status.idle": "2024-07-05T17:03:00.797028Z",
          "shell.execute_reply": "2024-07-05T17:03:00.796041Z",
          "shell.execute_reply.started": "2024-07-05T17:02:54.007171Z"
        },
        "id": "ZtxKbGG6L4GK",
        "outputId": "70748ac6-5bbe-4806-8e0f-1d1988a3991a",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "You are not running the flash-attention implementation, expect numerical differences.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'replies': ['{\\n    \"Car\": {\\n        \"Name\": \"Fiat Panda\",\\n        \"Manufacturer\": \"Fiat\",\\n        \"Designers\": [\\n            \"Giorgetto Giugiaro\",\\n            \"Aldo Mantovani\",\\n            \"Giuliano Biasio\",\\n            \"Roberto Giolito\"\\n        ],\\n        \"Number of units produced\": \"over 7.8 million\"\\n    }\\n}\\n']}\n"
          ]
        }
      ],
      "source": [
        "prompt=\"\"\"<|input|>\\n### Template:\n",
        "{\n",
        "    \"Car\": {\n",
        "        \"Name\": \"\",\n",
        "        \"Manufacturer\": \"\",\n",
        "        \"Designers\": [],\n",
        "        \"Number of units produced\": \"\",\n",
        "    }\n",
        "}\n",
        "### Text:\n",
        "The Fiat Panda is a city car manufactured and marketed by Fiat since 1980, currently in its third generation. The first generation Panda, introduced in 1980, was a two-box, three-door hatchback designed by Giorgetto Giugiaro and Aldo Mantovani of Italdesign and was manufactured through 2003 — receiving an all-wheel drive variant in 1983. SEAT of Spain marketed a variation of the first generation Panda under license to Fiat, initially as the Panda and subsequently as the Marbella (1986–1998).\n",
        "\n",
        "The second-generation Panda, launched in 2003 as a 5-door hatchback, was designed by Giuliano Biasio of Bertone, and won the European Car of the Year in 2004. The third-generation Panda debuted at the Frankfurt Motor Show in September 2011, was designed at Fiat Centro Stilo under the direction of Roberto Giolito and remains in production in Italy at Pomigliano d'Arco.[1] The fourth-generation Panda is marketed as Grande Panda, to differentiate it with the third-generation that is sold alongside it. Developed under Stellantis, the Grande Panda is produced in Serbia.\n",
        "\n",
        "In 40 years, Panda production has reached over 7.8 million,[2] of those, approximately 4.5 million were the first generation.[3] In early 2020, its 23-year production was counted as the twenty-ninth most long-lived single generation car in history by Autocar.[4] During its initial design phase, Italdesign referred to the car as il Zero. Fiat later proposed the name Rustica. Ultimately, the Panda was named after Empanda, the Roman goddess and patroness of travelers.\n",
        "<|output|>\n",
        "\"\"\"\n",
        "\n",
        "result = generator.run(prompt=prompt)\n",
        "print(result)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5kh320kJdARQ"
      },
      "source": [
        "Nice ✅"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZEIpUGBol1yj"
      },
      "source": [
        "### `PromptBuilder`: dynamically create prompts\n",
        "\n",
        "The [`PromptBuilder`](https://docs.haystack.deepset.ai/docs/promptbuilder?utm_campaign=developer-relations&utm_source=hf-cookbook) is initialized with a Jinja2 prompt template and renders it by filling in parameters passed through keyword arguments.\n",
        "\n",
        "Our prompt template reproduces the structure shown in [model card](https://huggingface.co/numind/NuExtract).\n",
        "\n",
        "During our experiments, we discovered that indenting the schema is particularly important to ensure good results. This probably stems from how the model was trained."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-07-05T17:03:00.800693Z",
          "iopub.status.busy": "2024-07-05T17:03:00.800275Z",
          "iopub.status.idle": "2024-07-05T17:03:00.819117Z",
          "shell.execute_reply": "2024-07-05T17:03:00.818214Z",
          "shell.execute_reply.started": "2024-07-05T17:03:00.800663Z"
        },
        "id": "uKIikH_wPrRk",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from haystack.components.builders import PromptBuilder\n",
        "from haystack import Document\n",
        "\n",
        "prompt_template = '''<|input|>\n",
        "### Template:\n",
        "{{ schema | tojson(indent=4) }}\n",
        "{% for example in examples %}\n",
        "### Example:\n",
        "{{ example | tojson(indent=4) }}\\n\n",
        "{% endfor %}\n",
        "### Text\n",
        "{{documents[0].content}}\n",
        "<|output|>\n",
        "'''\n",
        "\n",
        "prompt_builder = PromptBuilder(template=prompt_template)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2024-07-05T17:03:00.820820Z",
          "iopub.status.busy": "2024-07-05T17:03:00.820477Z",
          "iopub.status.idle": "2024-07-05T17:03:00.830614Z",
          "shell.execute_reply": "2024-07-05T17:03:00.829713Z",
          "shell.execute_reply.started": "2024-07-05T17:03:00.820786Z"
        },
        "id": "wJxenI9Ynr6y",
        "outputId": "b8e40999-46ec-46c0-c130-7bd137923799",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<|input|>\n",
            "### Template:\n",
            "{\n",
            "    \"Car\": {\n",
            "        \"Designers\": [],\n",
            "        \"Manufacturer\": \"\",\n",
            "        \"Name\": \"\",\n",
            "        \"Number of units produced\": \"\"\n",
            "    }\n",
            "}\n",
            "\n",
            "### Text\n",
            "The Fiat Panda is a city car...\n",
            "<|output|>\n"
          ]
        }
      ],
      "source": [
        "example_document = Document(content=\"The Fiat Panda is a city car...\")\n",
        "\n",
        "example_schema = {\n",
        "    \"Car\": {\n",
        "        \"Name\": \"\",\n",
        "        \"Manufacturer\": \"\",\n",
        "        \"Designers\": [],\n",
        "        \"Number of units produced\": \"\",\n",
        "    }\n",
        "}\n",
        "\n",
        "prompt=prompt_builder.run(documents=[example_document], schema=example_schema)[\"prompt\"]\n",
        "\n",
        "print(prompt)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-lcw0HOV-Ji-"
      },
      "source": [
        "Works well ✅"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eoz2t_UtI_XZ"
      },
      "source": [
        "### `OutputAdapter`\n",
        "\n",
        "You may have noticed that the result of the extraction is the first element of the `replies` list and consists of a JSON string.\n",
        "\n",
        "We would like to have a dictionary for each source document.\n",
        "To perform this transformation in a pipeline, we can use the [`OutputAdapter`](https://docs.haystack.deepset.ai/docs/outputadapter?utm_campaign=developer-relations&utm_source=hf-cookbook)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2024-07-05T17:03:00.831997Z",
          "iopub.status.busy": "2024-07-05T17:03:00.831691Z",
          "iopub.status.idle": "2024-07-05T17:03:00.847867Z",
          "shell.execute_reply": "2024-07-05T17:03:00.846811Z",
          "shell.execute_reply.started": "2024-07-05T17:03:00.831951Z"
        },
        "id": "shU6nb57F37v",
        "outputId": "c02f5c2f-0295-4859-cff7-a2a1e854855f",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'output': {'Car': {'Name': 'Fiat Panda', 'Manufacturer': 'Fiat', 'Designers': ['Giorgetto Giugiaro', 'Aldo Mantovani', 'Giuliano Biasio', 'Roberto Giolito'], 'Number of units produced': 'over 7.8 million'}}}\n"
          ]
        }
      ],
      "source": [
        "import json\n",
        "from haystack.components.converters import OutputAdapter\n",
        "\n",
        "\n",
        "adapter = OutputAdapter(template=\"\"\"{{ replies[0]| replace(\"'\",'\"') | json_loads}}\"\"\",\n",
        "                                         output_type=dict,\n",
        "                                         custom_filters={\"json_loads\": json.loads})\n",
        "\n",
        "print(adapter.run(**result))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IUH1MD2o-_F-"
      },
      "source": [
        "## Information Extraction Pipeline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9idAy7lR-_F-"
      },
      "source": [
        "### Build the Pipeline\n",
        "\n",
        "We can now [create our Pipeline](https://docs.haystack.deepset.ai/docs/creating-pipelines?utm_campaign=developer-relations&utm_source=hf-cookbook) by adding and connecting the individual components."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2024-07-05T17:03:00.849772Z",
          "iopub.status.busy": "2024-07-05T17:03:00.849310Z",
          "iopub.status.idle": "2024-07-05T17:03:00.861951Z",
          "shell.execute_reply": "2024-07-05T17:03:00.860945Z",
          "shell.execute_reply.started": "2024-07-05T17:03:00.849741Z"
        },
        "id": "_7g600GG-_F-",
        "outputId": "88d31346-291c-4f6a-8e45-1dca4b3997ca",
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<haystack.core.pipeline.pipeline.Pipeline object at 0x795de4121630>\n",
              "🚅 Components\n",
              "  - fetcher: LinkContentFetcher\n",
              "  - converter: HTMLToDocument\n",
              "  - prompt_builder: PromptBuilder\n",
              "  - generator: HuggingFaceLocalGenerator\n",
              "  - adapter: OutputAdapter\n",
              "🛤️ Connections\n",
              "  - fetcher.streams -> converter.sources (List[ByteStream])\n",
              "  - converter.documents -> prompt_builder.documents (List[Document])\n",
              "  - prompt_builder.prompt -> generator.prompt (str)\n",
              "  - generator.replies -> adapter.replies (List[str])"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from haystack import Pipeline\n",
        "\n",
        "ie_pipe = Pipeline()\n",
        "ie_pipe.add_component(\"fetcher\", fetcher)\n",
        "ie_pipe.add_component(\"converter\", converter)\n",
        "ie_pipe.add_component(\"prompt_builder\", prompt_builder)\n",
        "ie_pipe.add_component(\"generator\", generator)\n",
        "ie_pipe.add_component(\"adapter\", adapter)\n",
        "\n",
        "ie_pipe.connect(\"fetcher\", \"converter\")\n",
        "ie_pipe.connect(\"converter\", \"prompt_builder\")\n",
        "ie_pipe.connect(\"prompt_builder\", \"generator\")\n",
        "ie_pipe.connect(\"generator\", \"adapter\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-07-05T17:03:00.863446Z",
          "iopub.status.busy": "2024-07-05T17:03:00.863147Z",
          "iopub.status.idle": "2024-07-05T17:03:00.874010Z",
          "shell.execute_reply": "2024-07-05T17:03:00.873186Z",
          "shell.execute_reply.started": "2024-07-05T17:03:00.863420Z"
        },
        "id": "k-uK0sg5-_F-",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "# IN CASE YOU NEED TO RECREATE THE PIPELINE FROM SCRATCH, YOU CAN UNCOMMENT THIS CELL\n",
        "\n",
        "# ie_pipe = Pipeline()\n",
        "# ie_pipe.add_component(\"fetcher\", LinkContentFetcher())\n",
        "# ie_pipe.add_component(\"converter\", HTMLToDocument())\n",
        "# ie_pipe.add_component(\"prompt_builder\", PromptBuilder(template=prompt_template))\n",
        "# ie_pipe.add_component(\"generator\", HuggingFaceLocalGenerator(model=\"numind/NuExtract\",\n",
        "#                                       huggingface_pipeline_kwargs={\"model_kwargs\": {\"torch_dtype\":torch.bfloat16}})\n",
        "# )\n",
        "# ie_pipe.add_component(\"adapter\", OutputAdapter(template=\"\"\"{{ replies[0]| replace(\"'\",'\"') | json_loads}}\"\"\",\n",
        "#                                          output_type=dict,\n",
        "#                                          custom_filters={\"json_loads\": json.loads}))\n",
        "\n",
        "# ie_pipe.connect(\"fetcher\", \"converter\")\n",
        "# ie_pipe.connect(\"converter\", \"prompt_builder\")\n",
        "# ie_pipe.connect(\"prompt_builder\", \"generator\")\n",
        "# ie_pipe.connect(\"generator\", \"adapter\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UDZ4bgsa-_F-"
      },
      "source": [
        "\n",
        "Let's review our pipeline setup:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "execution": {
          "iopub.execute_input": "2024-07-05T17:03:00.875847Z",
          "iopub.status.busy": "2024-07-05T17:03:00.875327Z",
          "iopub.status.idle": "2024-07-05T17:03:02.008953Z",
          "shell.execute_reply": "2024-07-05T17:03:02.007956Z",
          "shell.execute_reply.started": "2024-07-05T17:03:00.875817Z"
        },
        "id": "beUceBIW-_F-",
        "outputId": "475c7e8b-9944-4f8a-ac14-a652f4e562ea",
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "image/png": 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1kOnIzs5Geno6ADCYaygGcw1nZ2dX5O/vz2AmA6XBTDUUx5iJiEwMg5mIyMQwmImITAyDmYjIxDCYiYhMDIOZiMjEMJiJiEwMg5nM0okTJxAdHY2kpCRjl0J0xxjMVOUKCwsxefJkBAcHw9fXF++//36l9y0qKsKGDRtQUFBwR8ecMmUK3nrrLZw+ffouKiYyLgYzVbnp06fjf//7H2xsbNC+fXu0adOm0vs+/vjjGDFiBAoLbzspNpHZ4EeyqcrFxMTAzs4Ou3fvRq1ate5o36tXr1ZZXUSmij1mqjIHDhyAq6srzp07h4KCAvj7+2PUqFHq+qysLIwfPx6NGzeGp6cnOnXqhPXr16vr27Zti4yMDABAw4YN4erqiu+//15dv3nzZvTs2RN+fn5o1KgR+vfvD41GY1DDDz/8gLCwMPj6+qJz587YsWOHwXqdTofPPvsMrVq1goeHB9q0aYPPPvsMOp1O3cbf3x+urq6YPn06QkNDUa9ePcycObNKzhkRGMxUlWrXro3IyEgAgKWlJSIjI9GyZUvg73soIyoqCitWrICLiwtat26N5ORkjBw5EsuWLQMAdOvWDfb2f9+f6cknn0Tfvn3h5+cHAPjyyy8xZMgQ7Nu3DwEBAfD19cXOnTuRnZ1tUENMTAysra3h6emJuLg4DBo0CMnJyUDpjCcjR47E9OnTkZ+fj7CwMOTm5mL69Ol48cUXy72eOXPmoH379oiIiMCgQYOq+OzRg4zBTFWmcePGWLVqFfD3XfCwatUqjB8/HgAwc+ZMpKamYsSIEThw4AA2b96MnTt3wtraGh9++CFKSkowbdo01KlTBwAwd+5cfPXVV3jkkUdw/vx5fPDBB7CwsMCaNWuwa9cu7N69G3/88Qc6depkUMPbb7+NvXv3Yt++fRg0aBB0Oh1Wr14NANiyZQtiY2PRokULHD58GLGxsTh8+DACAwOxZs2acr3vTz/9FAsXLsQPP/wAb2/vajqL9CDiGDMZxebNmwEA165dw+TJk9Xlzs7OyMnJQWpqKgIDAyvcd9euXSgsLESXLl3QsWNHdXmTJk3KbVvWQ1cUBb169cLKlSuRlpYGlAYzADg5OWH69OnqPmW99EOHDiE0NFRd3rdv33t+3USVwWAmozh//jxQOtRQkbJwvNW+DRs2vKNj2tjYAACKi4sBAJmZmQCAP/74A3/88Ue57e3s7AyeOzk53dHxiO4Wg5mMolatWigoKMC+ffvQuHHj226v1+vVf7u4uAA3BOu91AAAs2fPxvDhw++pLaL7iWPMZBTt27cHAHz22WcoKioCSnuyhw4dMtjO2dkZAHD8+HGg9AMnZfv+9NNPOHDggLptfHz8HX0QpUOHDsDfcycavGm4b9++e3hlRPeOwUxG8eabb8LR0RFr1qxBy5YtERUVheDgYAwePNggXB966CEAwIABA9C1a1e8/vrrCAoKwpAhQ1BcXIwePXrg0UcfxWOPPYYuXbqob+xVxsCBA9GkSRMcPXoUbdq0QWRkJNq0aYPu3bsjPj6+Sl43UWUwmMkomjZtis2bNyMyMhJ5eXk4fPgwnJyc0L9/f4Nhi/feew+RkZHQ6XRISUmBm5sbUHrp2pQpU+Dv74+UlBSkp6cjIiICzZo1q3QNDg4OiI2NxXPPPQcHBwccPnwYeXl56NevnzpcQmQMirELoLv35ZdfRrm5ua3q3LkzU4RUaWlpOHLkyNoRI0Y8Y+xa6O6wx0xEZGIYzEREJobBTERkYhjMREQmhsFMRGRiGMxERCaGwUxEZGIYzEREJobBTJWm0+ng4eGBv/76y9il1FgrVqxA27ZtjV0GmTgG8wMgPT0drq6uFQbqjh07EBISgtTU1Nu2c/LkSRQVFd30bnA5OTl455130LJlS9SvXx/NmjVDUlLSPdf/559/YsSIEffczq3ae//99+Hq6lru8c0339y2vWXLluGTTz6p1LGTkpIQEBBwt6XTA4K3/XwAaDQaWFhYoHnz5uXWNWjQAJGRkXB1db1tO0ePHoW7u7vBbTc9PDwAABkZGXjyySfh4OCAadOmISAgACkpKWjUqNE9179y5UpYWd2/H9WK2tNqtXjqqafw9ttvGyz38vK6bXtz587FK6+8UqljHz16FEFBQXdYcXl6vR4WFuxXmSt+Zx8AGo0GjRs3hoODg8Hy77//Hu3atUNcXBxcXFyQnZ2NkJAQfPPNNwgPD4eXlxd69+6N69evAwCSk5PVWUW2bt2KRx99FLm5uQCAl19+GXZ2duoEqU2bNkXv3r1ha2ur7vv000/Dx8cHzZo1M5jMtG/fvvj000/x1FNPwcvLC+3atUNiYiIAYMKECVixYgU2bdoEX19fbNy4EQBw7tw5jB07Vp3vb8iQIWotd9OeRqNBZGQkgoKCDB5OTk63PFZ4eDhOnjyJSZMmwc/PD1lZWUDpLCtdu3aFp6cnmjVrhqNHjwKlPeZz584hMjISvr6+ePbZZ1FYWKiei1WrVqFDhw7w9PREq1at1Mlpf/75Z/j5+WHmzJl46KGHsGDBgir4SSFTwWB+AGi1WoSEhJRb3r9/fwwYMEDtSdvZ2SEjIwN79+7FDz/8gC1btuD333/H9u3bgdLeXmBgIHbs2IGxY8di4cKFqFWrFo4cOYJffvkFn376qXr/5BulpaWhR48e6Nq1K44dO4ZFixbhk08+wd69ewEAV65cwdatW/Hxxx8jLi4Oer0eS5cuBQB89NFHsLS0RGxsLM6cOYPevXsjJycHUVFRKCgowK5du3Dw4EEcPnwYmzZtuqv2zp07h6ysLEyYMAG+vr7w9fXFnDlzgNLhmVsda+rUqXBycsKpU6dw+vRpuLu7Y+fOnRg8eDD69euH+Ph4xMbGws/PD1euXMG5c+dgYWGBpUuXYsuWLdi1axd++uknAMD8+fPx1ltvYeLEiUhJScHw4cPx/vvvA6WBnpeXBz8/Pxw4cMBgtnEyPxzKeABoNJoKx2gVRUFiYiIGDhwIlI4hK4qCzz77DM7OzvDw8IC1tbX6J3NycjLc3d0xatQoLF68GJ07dwYA7N69G+7u7oiIiKjw+B9//DEiIiIwbtw4AMCjjz4KLy8vJCQkqD3OhQsXqr88GjRooB4zLi4OlpaWBr9Y5s+fj+vXr2P+/PlwdHTE77//jitXrqhz/t1pe1qtFvb29tizZ4+6rOz2orc71l9//YXWrVsbDCtMnjwZI0eOVGfadnd3BwAcOXIEKJ0cwNXVFV5eXnByckJRURGuXLmC6dOn44033kCvXr2Qm5sLrVar3sY0MTER3bt3x4ABA4AKpr0i88JgNnNXr17FqVOnKuwx63Q6HDt2TP3Pn5CQAD8/P7XXe+bMGRQVFSEoKAglJSU4ceIEEhMTERISYjAJ6oULF9Twqcj27dvx3nvvqc9FBDk5OXBzc8OZM2eQm5uL4OBgdf3x48fRs2dPAMDBgwcRGhqqztcHAL/99hsURUGTJk2gKArq1auHOXPmoE2bNnfVXnx8PIKCgiocD7/Vscrau/Eqi6ysLCQmJqo97hslJSXB09NTHc/Pzs7G5cuXERQUhIMHDyIvLw8LFizA559/jqKiInTr1g3z5s1T9x06dOhNzzGZFwazmdNqtRARg9meyxw/fhyFhYXqUEZCQoLBG4RarRa2trYICAhAamoqCgsL8cMPP+CFF17A7Nmz8eabbwIA6tevjzNnziAvL6/cOLZer8f169dRv359ddmOHTtQUlKCxx57DPv370etWrXg4+MDAMjNzcWZM2fUOg4ePKjOdH2jkSNH4rXXXoNer4ejo6O6PCEh4Y7b02q1aNq06U3P4c2OhdKZtAcNGqQ+v3btGnCTyWSTk5MNzm9CQgKsrKzQpEkTZGdnQ1EUxMfHIy8vD7Vq1VJ74WW/QCt685bME8eYzZxGo0HdunWRk5ODlJQU9VFcXIzExETUrVtXDc3ExESDnqZWq0VQUBCsrKyQnJyM2rVro2PHjvjvf/+LWbNmIS4uDgDQs2dPFBUV4fnnn8eff/6JP/74A++88w4OHz4MCwsLBAcHY926dcjPz0dycjImTpyIf//733B1da3wlwEAtReflZWF1NRUZGZmIiMjAwAQFhaGNWvW4NixYygoKMAvv/yi7n837cXHx980mG91LJ1Oh0uXLiExMRHnzp3DlStX4OvrC3d3d8yYMQMZGRnQaDTYv38/UNrrvfH8JiYmIiAgALa2tggNDYWtrS3mzJkDvV6P5ORknDx5EgBw4sQJFBYW3tHsLFSzMZjNnFarxcWLF/HII4+oj/bt20On0yExMbFcD+5mz48ePaoGQ1RUFAYNGoRx48ahoKAAjRo1wsqVK5GZmYlnnnkGI0eORHZ2tjo0MG/ePJw6dQqBgYEYOnQoxowZo/a2KwpSb29v9ZK8kSNH4sCBA2jbti1+/PFHAMAbb7yB0NBQ9O7dGw899BC2bdt209dwu/auXr2KtLQ0dcz4n251LCsrK4wZMwZz585FeHg4Tpw4ASsrK3z99ddIS0tD27ZtMWTIEFy9ehUo7THfGMw3BrW7uzu++OILxMTEIDg4GCNHjlQnqU1KSoKHh0elLmkk88CppWowTi1FFeHUUjUfe8xERCaGwUxEZGIYzEREJobBTERkYhjMREQmhsFMRGRiGMxERCaGwUxEZGIYzEREJobBTERkYnh3uRouNzfX9s8//7xm7DpMgCIiiqIoemMXYmzXr1+3FpEH/jzUZAzmGkxENIWFheNOnTpl7FKM7uzZsx/r9XpvNze3SQ4ODunGrsfYLCwsjhu7Brp7vIkRmYXnn39eAyBEREKio6MTjF0P0b3gGDMRkYlhMBMRmRgGMxGRiWEwExGZGAYzEZGJYTATEZkYBjMRkYlhMBMRmRgGMxGRiWEwExGZGAYzEZGJYTATEZkYBjMRkYlhMBMRmRgGMxGRiWEwExGZGAYzEZGJYTATEZkYBjMRkYlhMBMRmRgGMxGRiWEwExGZGAYzEZGJUYxdAN0bEfECkG7sOsikvKUoygxjF0F3jz1mIiITw2AmIjIxDOYHwKlTp5CWlmbsMoiokhjMZu7EiRNo1KgR/vrrL2OXQkSVxGA2c8XFxdDr9ZXaVkSqvB76G881kRkTEa/r16/LsGHDxNXVVVxdXaVPnz6SlpYmqampAsDgMWzYMBERiYmJEQCyfv166dChg1hbW8ukSZNEROT69evy2muvSb169cTZ2VnCwsJk1apVUubMmTPy3HPPibu7u9jY2EhISIh8++236vrDhw+Ls7OzxMbGSosWLcTa2loCAgJkyZIlMm3aNPH19RVnZ2fp06ePXLhwQd1v06ZNEhISIvb29tK8eXOZN2+e3KmNGzfKsWPH7mifo0ePSufOncXR0VF8fHzkhRdekJKSEhERKSoqkokTJ4qXl5fY2NhIy5YtZd26deq+7777rtja2hq0d+DAAQEgW7ZsERGRl156SerXry8bN26Uxo0bi6Iosn37dhEROXXqlAwZMkTc3d3F1tZW2rVrJ6tXr1bbSk1Nlb59+4qTk5O4u7tLt27d5MCBA7c7Z28a++eS6IEmIl6TJk0SRVHkww8/lMWLF0tERIRkZ2dLQUGBfPvttwJAPvroI9mzZ4+kpKSI3BDM3t7esnTpUtm5c6ecPXtWSkpKpEuXLtKoUSOZM2eOfPvttzJ69GgBIF999ZWIiJw8eVKaNm0qkydPlv/+97/y+OOPi6Iosn//fpHSYAYgfn5+EhsbKzt27JCQkBABIBEREfL777/LypUrxcnJSYYMGSIiIlevXhV7e3sJCwuTFStWyFtvvSUTJ068s1QWkRkzZoi1tbUMGzZMjh8/Xql9IiIixN3dXRYtWiSzZs2SXr16qeuGDx8uVlZWMmXKFImJiZE+ffqIoijy66+/3lEw29nZSWhoqGzbtk3WrVsner1eMjIyxNPTU+rVqyczZsyQpUuXyqhRo2T27NkiInLu3Dnx9PSUxx57TL766itZsmTdPG+9AAAgAElEQVSJdOzYUezs7ESr1d7qnDGYiYxJRLyGDBkiTk5OUlRUVC50kpKSBIDExMQYLC8L5k8++cRg+ffffy82NjaSnp5usHzgwIHSokUL9bler1f/fe3aNbG3t5d33nlH5IZg/v7779Vtvv76awEgWq1WXTZy5Ejx8PAQEZHjx48LAJk2bVplsvSWtFqtDB06VOzt7WXEiBFy4sSJW27v4+MjkZGR5ZYnJycLAHnvvffUZXq9XgIDA6VTp04idxDMAOTPP/802G7s2LFiZWUlycnJFdb14osvSqtWraS4uFhdVlRUJH5+fjJ+/PhbnTMGcw3HMWYzMHjwYOTl5aF79+7QaDR3tG+XLl0Mnm/evBnFxcVo1KgR7Ozs1EdMTAzOnj2rbnfkyBH06dMH3t7eCAoKQklJCTIzMw3asre3V/9tZ2cHALC1tVWX+fj4IDs7GwDQqFEjtG/fHlOnTsW8efNQWFh4y7oLCgqQlpamPkpKStR1wcHBWLZsGZKTk5GXl4cmTZogMTHxpm0NHToUP//8M1555RVcuHBBXb57924AQL9+/dRliqIgMjLyjt9MdXR0xMMPP2ywbMuWLejcuTOaNGlS4T6bN2+GRqOBk5OT+n1wdnbGmTNncPbs2Ts+Z1RzMJjNQFRUFDZt2oTMzEy0bNkSY8aMQXFxcaX2dXJyMniemZkJDw8PxMXFGTy0Wi0OHDgAANi5cyfatWuHwsJCLFmyBGvWrIGrq6tBOFaGoijqm2CKomDLli0YNmwYXn/9dQQFBeHXX3+96b5//vknGjZsqD6ysrIM1qelpeGTTz7Bpk2bMGzYMHh7e9+0ralTp2LOnDlYvXo1GjVqhPnz5wMArly5AgCoV6+ewfaurq64evUqrl69WunX+s/zDADnz5+Hr6/vTffJzMxEr169yn0vEhMTMX/+/Ds+Z1RzWBm7ALo/oqKiEBkZif/+97/4z3/+A39/f7z77rvq+speBVCnTh1kZWXB399f7eX+00cffYTAwEDExsbCyurvHyFHR8d7fg21atXC/Pnz8frrr6NPnz7o06cPzpw5U2GohYSEYN26dQZ1A0BKSgo++ugjxMTEYMCAAYiLi0NAQMAtj6soCl577TWMGjUKL7zwAl5++WW0atUKPj4+AICLFy/Cy8tL3f78+fOwsbGBg4MDFOXu72pQu3btcn9l3KhOnTrIzs5G06ZNb7pNRefs5MmT/H9dw7HHbAbK/oS1sLDAv//9b3h7e+PQoUPADYGZkZFRqba6du0KnU6HBQsWGCy/fv26+u/s7Gy0bNlSDeXCwkJcu3at0pfl3UxBQQEAoGHDhhg/fjwuX76M1NTUCrd1c3PDU089pT7Khkjmz58PnU6HI0eOYNmyZbcN5RuP6+zsjA8//BAAcOjQIbRr1w4WFhbYvHmzum1hYSFiY2MRHh4OS0tL1KtXD4WFhcjJyVG3qeyHeTp37owdO3aU216n0wGl34s//vgDBw8eNFh/4/eionOWmJhY/jcZ1Sj8zWoG5s6diw0bNmDo0KHIyMhAeno6wsLCgNJx3EaNGmHWrFlwdHRETk4Oxo8ff9O2hgwZgoULF+KNN95Aamoq2rRpgyNHjmDdunVISkqCvb09OnXqhKVLl2LJkiWoW7cuZs+ejZycHGi12ru+PreoqAhNmjTBs88+i+DgYHzxxReoXbt2pYL1RtOnT4eDg8Md7dO/f3/UqlULkZGRagi3bdsWAQEBGDZsGKZMmQKdToeAgAAsWrQI58+fx4oVK4DS8LSwsMCrr76K1157DVqtFm+99Valjjt58mT8+OOPCA8Px/jx4+Hh4YGff/4ZTk5OiI6Oxvvvv49NmzYhMjISEyZMQL169bB161bodDqsX7/+pucsJCSk8mMsRHT/iYjX2rVrJSwsTBwcHMTT01NeffVVgys09u/fL8HBweLo6ChNmzaVtLQ09aqMpKSkclcDXLlyRcaOHStubm5ia2srISEhMn36dLXNK1euyDPPPCMODg7i4+MjH374oWzYsEE8PDxkx44d6lUZP/74o9rmypUrBYDBNcZTpkwRS0tLERHJycmRESNGiKenpzg4OMjDDz8se/bsqfBqhfvtww8/lMDAQLGzs5PAwEBZtGiRui4/P19ee+01qV+/vtjY2Ejr1q0lNjbWYP9ly5ZJQECA2NvbS2RkpCxdurTC65grEh8fL1FRUeLo6Ch16tSR8PBw+e6779T1SUlJ0rNnT3FwcBAnJyd57LHH1CtsbnHOeFVGDcfbftZwvO0nVYC3/azhOMZMRGRiGMxERCaGb/7VfCUATt3D/goAy/tYj1Hk5ubW1+v1VrVq1TpvYWGhM3Y990hf+rhbV+5jLWQEHGN+wIlIPwBrjV0HGVinKEq/SmxHZopDGUREJobBTERkYhjMREQmhsFMRGRiGMxUpaZNmwZFUaAoCsaNG2fsckyam5tb2bnqC6CZsesh42EwEwBg+fLlcHJyqvBGRO+//z7atGlz2zaio6MxZcoUg2VarRZdu3bFuXPnMHPmTADApEmT4OLiAhsbG3h5eWHo0KHlbttZkXPnzuGpp57CmTNnKvWaRAQffvgh/Pz8YGNjg/r16yM9/f8/JPnyyy/jhx9+qFRb1UGr1WLWrFmwsrLSAzhm7HrIeBjMBACIi4tDaGgoLCzK/0i0bdsWPXv2vG0bM2bMKHffY61Wi9atW8PDw0O9093u3bvRr18/HD9+HBs3bsT+/fsxderU27a/c+dO7N+//5b3ML7R0qVLMWfOHCxatAgnTpzA8uXL1frS09Mxf/58hISE3HT/O72/9L3y8PBAeno6fH19rwGo6ddiE9HdEpF+IiKdO3eWcePGlbvJzqhRowSATJgwQaR0ItYePXqIk5OT+Pr6qjfUadasmQAQR0dHcXJykvPnz0txcbHY2NjI0qVL1fb0er04OzvLd999JzqdTg4ePCheXl6yZMkSGT16tDzxxBMGx3/88cdl4MCB8u2334qVlZVYW1uLo6OjvPrqq+o233zzjYSEhIitra34+/urk5m++OKL0qVLl3Kv6fTp02JraysWFhbi6Ogobdq0ERGR2NhYcXJyko8++kgaN26sTlxbWFgo77zzjvj6+oqtra20bdtW4uPjRUqn1Xr11VelXr16Ym9vL40bN5a1a9eKXq+Xxo0by+uvvy6hoaFiZ2cnYWFhsmzZMmndurXY2dlJp06d5Nq1awa1devWTZ544onK/UlAROapLJjr1q0rCxcuLBdiJSUl4uvrq4br008/LT169JCMjAz55ZdfJC0tTUREfvrpJ3FyclJnlxYRSUxMFAAGszofO3ZMAIiiKOrXstm5P//8c/Hz81O3jYuLEwCyd+9eEREJDw+XGTNmGNQ3c+ZMcXZ2lh9++EFyc3Nl+vTp4u/vLyIi69evF0tLS5k7d2651zVx4kTp0aOHwbJPPvlELCwsZPny5Wroiog8+eSTEhwcLAcOHJDc3Fx58sknZfjw4aLX6yUyMlK6du0q6enpkp+fL/7+/hIdHS0iIu7u7vLoo49KWlqaJCYmiq2trTz++ONy9uxZ+e2339RZym/k7e0tL7/8coKxfy6IyIhEpN/p06cFgDrL9Y0uXbokAOTgwYMiIvLUU09Jq1at5PTp0wbbffDBB+oEpWW+//57URRFrl+/brCsTp06UlJSIhcvXpS33npLLCws5NChQ7Jt2zaD7UeMGCHt2rUTKZ2E1M7OTnbv3m1Qm4ODgzqh7OXLl2XgwIEGs1yvWLFCnJycpHfv3gZ1dOrUST744AODegcPHixPPfWUwbKffvrJYCLVkydPSvPmzWXmzJnqTN8XL15Ug9zS0lKOHDkieXl5YmFhod66tLCwUKysrGTbtm0iIlJcXCwWFhby888/q8e6fPmyAJBFixbtNfbPBREZkYj027hxo1haWkp+fn65YP71118N1l24cEH69+8vdnZ2Br3XHj16yNtvv22w75QpU6RRo0YGyyZOnCgRERHqc71eL46OjvK///1PMjIyBIDExcXJhQsXxM7OTr038f79+8XS0tLgT/+y0Kxfv77Url1b7O3tZcCAAXLhwgWDYx47dkz8/f3VoZqSkhJxdnaWrVu3GmzXsmVLmTdvXrl6nZycxNnZWVxcXMTV1VXefvttKSkpkT59+shzzz2nbrtr1y5xdHQUnU4n+/fvFwBy+fJlkRtmDj9//ryIiGg0GgEgGRkZ6v6///67AJDDhw//ZOyfCzIu3sSIEBcXh6ZNm1Y4x198fDwCAwPVde7u7vj++++xePFijBkzBiNGjICbmxv279+P4cOHG+yr1WoRHBxc7lg3vuGWk5ODgoICuLq6wtPTE3Xq1MHRo0fx448/wtXVFc888wwAYN++fWjSpEm5uQUVRcHp06dx/fp1uLi4VPjmZWBgIJ5//nl11pHExERcvXrV4EoTnU6H5ORkhIaGltu/devW2Llzp3qMMqmpqXj66afV5xs2bECrVq1gaWkJjUYDf39/dfv4+Hh4eHioE7vGx8fDzc0Nnp6e6v4JCQlwcHBAaGjoddADjVdlEOLi4tCgQQMkJyerjxMnTgAANBqNGlZJSUlYsWIFsrOzkZGRATc3N9SuXRs6nQ45OTmIj49HRkYGLl++DNwkmA8fPgxvb29kZGRgy5Yt6NatG3x8fNCrVy8AQJs2bbB27VosWLAAL774IqytrQEAFy5cQFZWFlJTU3Hy5EkAQKtWrWBra4tp06ZBr9cjISEBx48fB0pnmF64cCH27duHn376CcuXL1en27pw4QIA4ODBgzh27BhEBMeOHUNhYWG5qzQeeeQR7Nu3D5s2bYJer8f27dvVefZ8fX1x9OhRAMDevXvx5ZdfwtnZGSgN3hYtWqjt3O552flq1qwZLC1r/M3+iOheiEi/gIAAAWDwCA8PFxGR9u3bq2Oxy5YtE29vb3FwcJD27dur464iIuPHjxcbGxtxcXGR/fv3S0FBgVhaWsqKFSvUbTIzM9X27ezsJCgoSF566SVJT09Xt4mJiRFra2uxs7OTrKwsdblWqxU/Pz+xtraW/v37q8tXr14tgYGBYmtrK02bNhWtVisiIn/88YeEhISIvb291K9fX0aNGqUOK+Tl5UmHDh3EyspKPD09Ra/Xy+rVq8XT01MqMnHiRKlfv744ODhIeHi46PV6ERE5cuSIhIaGir+/v7Rp00b69+8vDRo0ECm9yqXsTU0RkcjISHn99dfV5z169JDXXnvN4DhdunQpuxLEdC6uJqLqV3ZVxv1WdkXF4cOH73jftm3byqhRo6qiLJPm4eFRNm7PYH7AcYyZqoRWq4WiKKhduzauXLliMDZbEb1ej61bt2LVqlXQarVYvXp1tdVqbNnZ2cjJyUFmZuYtP/BCDw6OMVOVSEhIgIigYcOGeP/992+7fX5+PgYMGICjR48iNjYWAQEB1VKnsYkIGjVqhCZNmgAAg5kAzmBCItINwEJj13GvLl++7FVSUmJdu3btDEtLy2Jj13OPtiqKMtbYRZDxMJjJLDz//PMaACEiEhIdHc1PzlGNxqEMIiITw2AmIjIxDGYiIhPDYCYiMjEMZiIiE8NgJiIyMQxmIiITw2AmIjIxDGYiIhPDYCYiMjEMZiIiE8N7ZVCNM2bMGL2iKIqIlFunKApuWL5dUZSuixYt4s851SjsMVONoyjK+dKv5R7/WN4VgM7Y9RLdKQYz1UQfAyi83UalE4McrJ6SiO4fBjPVODqdbtntgrl0OEMsLCz+XW2FEd0nDGaqcZYsWXIVwLsicqsb4ouiKIcWLly4txpLI7ovGMxUI126dGkRgOsVvQFYukwvIpONURvRvWIwU40UExNTpCjKZwAq6jULgAPR0dFbjFAa0T1jMFONlZ+f/18A12+y+r1qLofovmEwU421fPny64qiTBKR4rIhDb1eL4qiJERHR283dn1Ed4vBTDXapUuXvgSQg9KxZUVR9Hq9/j/GrovoXjCYqUaLiYkpKR1rzpe/u81xixcv3mbsuojuBYOZarxLly59ASDfwsJCsbCw+MDY9RDdq1veQ2DhwoVP2djYDKm+coiIzJ9er08bNWrU6zdbb3Wb/Vu5ubk97e3tff8rIyJ6ABUUFCApKSkDwF0HM+rUqQN/f//7XhwR0YMoNzcXSUlJt9yGY8xERCaGwUxEZGIYzEREJobBTERkYhjMREQmhsFMRGRiGMxERCaGwUxEZGIYzESlLl26hNjYWGOXQcRgJgKA9PR0NG/eHDNmzDB2KUQMZiIAKCwsRGHhLSfeJqo2DGa6JxcvXsRbb72FFi1awNPTEw899BBmzZoFnU4HAEhJScG//vUvNGjQAD4+Pujduzf27dun7q/RaODq6op3330Xzz77LBo2bIjg4GC88cYbuH7971mjevfuDVdXV6xatUrdT0TQpk0buLq6QqPRAACysrIwfvx4NG7cGJ6enujUqRPWr19f7lgdOnTA2LFjERAQAE9PT2zYsAFhYWEAAK1WC1dXV7i6uiI9PV3dd/369Xj88cfh6emJJk2aYPz48cjOzlbX+/v7w9XVFdOnT0doaCjq1auHmTNnVum5J/PFYKa7lpOTg8jISERHR6OwsBCtW7fG5cuXsW3bNlhZWeH06dOIiorC1q1b0bBhQ4SEhOC3335Dnz59cOjQIYO2FixYgNTUVPTp0wf29vb46quv8O677wIARo8eDQAGwfzLL78gLS0N4eHhCA0NxaVLlxAVFYUVK1bAxcUFrVu3RnJyMkaOHIlly5YZHCspKQk7d+5Ez5490bVrVzz66KOIjIwEALi4uKBv377o27cvHBwcAABffvklRo4ciePHj6NNmzZwdHTEihUr0LNnT1y7ds2g7Tlz5qB9+/aIiIjAoEGDqujMk7m77d3liG5m5syZSE1NRefOnbF8+XLY29sjPz8fWVlZAIAZM2bg8uXLGDFiBGbNmgUAmD17Nj7++GNMnz4dMTExalsBAQH45Zdf4ODggIsXLyIkJASrVq3CrFmz0LNnT3h5eWHPnj04e/YsfHx81LB9/vnnDWoZMWIEZs6cCUVRkJSUhI4dO+LDDz/E4MGD1WNZWlpi48aNaNq0qbps2rRp+Pnnn+Hr64uvvvpKXX7hwgV88MEHcHZ2xo4dOxAYGAgRwdixYxETE4Ply5dj3Lhx6vaffvopRowYUaXnncwfg5nu2pYtWwAAb7/9Nuzt7QEA9vb28PPzA0p7tQAwZswYdZ/Bgwfj448/xt69ew3acnNzU3uodevWhZ+fH44dO4Zz587Bx8cHw4cPx7Rp07B69WoMGzYMW7Zsgbe3N3r16gUA2Lx5MwDg2rVrmDx5stqus7MzcnJykJqaqi5r2rSpQSjfys6dO1FYWAgPDw9888036vKrV68CQLmef9++fSvVLtGtMJjprp0/fx4A0LBhwwrXX7x4EQBQv359dZmbmxsAIC8v75Zvttna2gIAioqKAADDhg3DzJkzsWrVKlhbW6OoqAijR4+GpaWlQS039sJvVNabBwAnJ6c7fo2nTp3C/PnzK2z3RnfSNtHNMJjprrm4uKCgoACZmZmoW7duufV169ZFRkYGsrKyUKdOHQBAZmYmUBpgZeFbGe7u7ujduzfWrFmD2bNnw97eHs8995y6vlatWigoKMC+ffvQuHHjCtvIycm57XH0er3B81q1agEA+vXrh8WLF1e6XqJ7wTf/6K516NABKB3fLev9FhUVIS4uDgAQEREBAAZDAAsXLgQAPProo3d8vLIhkdzcXDzzzDNq2ANA+/btAQCfffaZ2ssuLi4uN9RwM87OzgCAs2fPqj3roqIitd0tW7YYtBUfH4+8vLw7fg1ElcFgprv2xhtvwMnJCRs2bEBoaCi6deuGkJAQDB06FDqdDhMmTICjoyMWLFiATp06oXPnzvjf//4HOzs7vP3223d8vIceeggtW7YEALzwwgsG69588004OjpizZo1aNmyJaKiohAcHIzBgwejoKDgtm27u7ujYcOGyM3NRbt27RAREYHVq1ejSZMmGDRoEPLz89GtWzd06tQJERER6NKli8EvHKL7icFMd61JkybYunUroqKiUFhYiPj4eDg5OeHZZ5+FTqdD48aNERsbi06dOuH48eNISUnBo48+io0bNyI0NPSujjlq1ChERESgefPmBsubNm2KzZs3IzIyEnl5eTh8+DCcnJzQv3//csMTNxMdHY0WLVogKysL586dU4dn5s6di0mTJqFBgwZISEjAmTNn0KFDB4SEhNzVayC6HeVWKxcuXPh+cHDwlH/+JyAioruTm5uLHTt2ZIwaNcr7Ztuwx0xEZGIYzEREJobBTERkYhjMREQmhsFMRGRiGMxERCaGwUxEZGIYzEREJobBTERkYhjMD7g5c+bgoYceMlhWXFwMLy8vxMTEYPz48epUS/98BAcHq/tERUUhJSVFfS4i6Ny5M5KTk9Vl48ePR//+/dXnq1evvmnbrq6uOHHiRIU1x8fHIygoCJ6enmjVqhVGjBihTi9V02VmZmLIkCEG01rRg4e3/XzAaTSacsGs0WhQUFCA0NBQtG/fHi+//DJQep+K0NBQvPbaa8AN9yI+ceIE9u/fj+TkZAQFBQGlc+TFxcUZ3EBIo9GgU6dO6vPIyEj8+eefAID58+dj3759BtNANWrUqMKaDx8+DEVR8NNPPyErKwvz589H165dsW7dOvVucDXVnj17cOjQIXh73/TTuvQAYI/5AafVatGuXTuDZX/99Rfs7OzQuHFjeHt7IygoCI0bN8apU6cQHh6OoKAgBAUFwdfXFwCwcuVKAMDRo0eB0h731KlTgRtudK/T6ZCcnIwWLVqox6lTp47aVmZmJlq1aqU+r1evHl599VUEBASgYcOGGDNmjDq/nkajQWhoKFq0aIEuXbqod4GbO3cuUHpP5dmzZ6NFixbw8fFBjx49DHrzu3btQteuXeHp6YlmzZrh6NGjWLduHXx8fAxueNSyZUssWLAAIoKHHnoIkydPRkREBLy8vNTjduzYEV5eXujTp486eWxRURE+/vhjhIaGwtPTE507d0ZiYiIAYPr06Rg3bhxeeeUVNGjQAI0bN8aaNWsAAGvWrMFLL72EixcvwtfXF++8804VfMepJmAwP8Dy8vJw8uRJvPPOO/D19VUf7733Hpo1a6bODgIAJ0+exLVr18rdFU6v12PVqlXw9fVVhy2++eYbtadc9jUlJQWFhYU3vSObVqtV2y4qKsLTTz+NwsJC/PXXX9izZw/279+PBQsWAKVDGTfWYW1tjQ4dOuDUqVMAgHfffRcbNmzAmjVrkJycjNq1a+PNN98ESqeKGjx4MPr164f4+HjExsbCz88PiYmJaN68OSws/v4vkZubizNnziAkJASKouDKlSs4ePAgVq5ciV27diExMRErVqzAd999h3Xr1mHPnj3YvXs3AGD48OHYvHkzli1bhpSUFHh4eOCLL75Qz/mOHTvQo0cPaLVaPPbYY5g9ezYA4JlnnkHr1q0xadIknDlzBtOmTbsv32eqeRjMD7CEhASICHbu3Indu3erD39//3IBqtFoYGVlhWbNmhks/+WXX3D58mW8+eabOHr0KK5du4aZM2diypQpsLCwUHvMGo0GTk5OFQ5PXLx4EefOnVOP+d133yE9PR3z5s1DnTp14OPjgw4dOiAxMRF6vR5JSUnlfkFcunQJtWrVQkpKChYtWoQvvvgCQUFBcHJyQs+ePZGQkAAAmDx5MkaOHIkXX3wR7u7uCAgIgL29PRITEw3GzMt6uMHBwcjPz0dOTg7ee+89+Pr6omHDhigpKcGECRPg5eWFtm3bwsLCAvb29ti5cye2bt2KuXPnonXr1up8g2Xn7eTJk3j22WfRvXt3ODs7Izg4WP1lUFxcDI1Gg7Zt296X7y/VXAzmB5hGo0GDBg3QtGlTNGrUCI0aNYK/vz/OnDlTLvi0Wi0aN25cbjqo7777Dn369MEjjzyCEydOYN68efD19cUzzzwDa2trtces0WgMeqQ3io+PBwD1mNu3b0d4eDhsbGzUbbKzs+Hm5oYTJ07g+vXrBiFaVFSE3bt3IywsDDt27EDdunUN1l+8eBFubm7IyspCYmIi+vTpU66GxMREg19GCQkJ8PLygqurK5KSkqDX69V7QB89ehQ6nU49xrFjx6DX69GsWTP8/vvvcHJywtNPPw1/f3907twZ3bt3V2fSTkhIMKjtxIkT6ri8RqNBcXGxOhkAPbj45t8DTKPRlJstOjU1VX3j75/b/nNZbm4uNm/ejLVr16Jhw4awsrLC559/jh9//BGKoqiTpqI02G8cX76RVqtVQ7Cs3RvDKycnB7///jtGjx4NjUYDe3t7BAYGqus/+OADXL58GaNHj8b3339vMPkrAPz444944okn1DHqf06gev36dZw+fdrgr4H9+/erQZ2YmAg/Pz91/r+EhATUr18f7u7u6vO6devCw8MDKP0Fs2HDBuTl5an7oHRm7TNnzhjc5F+r1eLJJ58EABw8eBCBgYFwdHSs8DzRg4M95geYVqstF8zJyclQFKXcDCE3jgGXWbt2LXx9fREeHg4LCws0bdoUvXr1Ut9MvLHHrNVqUbduXaSkpKiPsolZ/xn6oaGh2LlzJzIzM3Hx4kWMHz8eYWFheOKJJ6DRaODr64uUlBRs27YNQ4YMwddff42FCxeiYcOGaNGiBY4dO4a//voLBQUFmDlzJs6ePYuXX34Zvr6+cHd3x4wZM5CRkQGNRoP9+/ejqKgIImIw0/a6desMgvnGXxT/7PXe+DwsLAwHDx7Ezz//DL1ej19++UU9B4mJibCwsECTJk2A0jdEjx49qp7r7OxsXLx4EadOnUJaWto9f3+p5mIwP6DKxmrLQqJMcnIyGjZsqE5OigrGgMusXLkSQ4YMUZ+3adMGU6ZMUZ/b2HBe9YsAACAASURBVNigsLAQ6enpyMnJwaeffopHHnlEfURHRwMVhP7rr7+OoKAgPPzww4iIiICPjw++/fZbKIoCjUaDlJQUPP7445gwYQJcXV2xe/du9O7dGwDQvXt3jB07Fv/617/QrFkzxMXFYdOmTahXrx6srKzw9ddfIy0tDW3btsWQIUNw9epV1KlTB6NHj8Yrr7yCtm3bIiEhAdbW1mrY/jOYk5KSDH5x3bi+e/fueOmll/Cf//wHISEhmD59ujr8k5CQgMDAQPV5SkoKioqK1Lb69u0LOzs7tGvXDh988ME9fX+pZuPUUkRE1YhTSxER1UAMZiIiE8NgJiIyMQxmIiITw2AmIjIxDGYiIhPDYCYiMjEMZiIiE8NgJiIyMQxmIiITw2AmIjIxDGYiIhPDYCYiMjEMZiIiE8NgJiIyMQxmIiITw2AmIjIxDGYiIhPDYCYiMjEMZiIiE8NgJiIyMQxmIiITw2AmIjIxDGYiIhPDYCYiMjEMZiIiE8NgJiIyMQxmMisRERGYO3euscsguicMZipn/fr1eOKJJ+Dt7Y3mzZtj3LhxuHDhQqX3f/PNN/Hjjz8aLPv000/RsWPHKqj2/xUUFCAlJQUtW7a86Ta9e/fGO++8U6V1AEBmZiaGDBmC9PT0Kj8WmR8GMxmYM2cOxo4di6ioKGzZsgUzZ87Evn37MHr06Ertn5GRgcWLF6NZs2YGy1u2bInIyMgqqvpvCQkJ0Ol0twzmzp07IywsrErrAIA9e/bg0KFD8Pb2rvJjkflhMJPq0KFDmDp1Kj777DNMmDABLVq0QI8ePTB58mT89ttvyMnJwerVq9GlSxdMmDABjRs3RrNmzfDpp58CANLT09G2bVtYWFigU6dO6NSpEwBg/Pjx+Ne//oX8/Hz1WJs2bcKjjz4KLy8vPPzww9i6dSsAIDs7GyEhIfjmm28QHh4OLy8v9O7dG9f/j737Doviav8G/l0WAWkCAtJ7lSKIiF2DNZpYosYYMdhrLIktauy9x8QeFJ9oVNRYYoxGMdGoiVFRhGV36U2agAqo9L3fP152fgwsTVFWPZ/r8kp2dubM2TnDvWfOnL3n+XMAwPPnz7Fo0SI4OzvD3Nwcvr6+XO/8wYMHsLGxgZ6ensLP5+Pjg5UrV0JLSwsAsG7dOkydOhUzZsyAtbU1HB0dcfLkSQBATEwMXF1dsWnTJnh7e8PGxgbjxo3Ds2fPAAAdOnTA9u3bubKPHDkCW1tbAMDJkycxffp05ObmwtLSkuuhBwcHo02bNrCwsMDQoUNRVlYGAJg2bRp8fX1RVFTUyC3KvK1YYGY4u3fvhoODA0aNGsVbbmZmBgDIzs5Gfn4+EhMT0adPH4SFhWHBggXYsGED/vvvP5ibm2PatGno2bMnUlNT8ddffwEAvvvuO25YBAB+/fVXzJgxA6tWrUJiYiJGjBiByZMn48WLF9DQ0EB6ejr+/fdfnDp1ChcuXMDNmzcRGhoKIkJgYCCkUimuXr2K+Ph4lJaW4unTp0BFYK6tt3z8+HEA4Orx4sULXLlyBf3794dIJEK3bt2wdetWAEB5eTmysrKgqamJq1evIiQkBFeuXMHevXtRXFyM+Ph4uLu7c2VHRUXBzc0NADBs2DB4e3vj22+/RWpqKtauXQuJRII5c+Zgy5YtuH//PsaPHw9VVVUAgI6ODlq0aAGhUNiIrcm8zVhgZjg3btzAgAEDoKLCPy1SUlIAAK1atUJaWho++OAD9O3bF7q6uhgzZgx0dHQQExMDALh79y58fHx42xcUFCAtLQ2tW7dGeXk5Fi1ahAULFqBHjx5QV1fH0KFDUVBQgJSUFCQkJEAgEGDTpk0wNTWFp6cnmjVrBhUVFZw+fRq3b9/G/v37YWpqivLycqSlpaFt27ZARWD28vKq8fNJpVLo6OjA0tISAJCQkIARI0bgww8/hI6ODtzc3LjPnpaWBmNjY0yfPh0tWrSAn58fOnXqhJiYGERHR6O8vJwLxAAgFou5QF1aWorIyEjecZD3jpOSkmBoaIj+/ftz723YsAGhoaFo1qzZS7cd825hgZnhZGdnw8LCotryS5cuwc3NDXp6epBIJLyAVFJSgufPn8PAwAAymQzh4eHVArNYLIZQKISzszPEYjHS09PRvXt37v2cnBwAQMuWLREVFQUrKyvo6OgAAFJTU1FSUgInJyecOnUKH330EfT19QEA9+/fh4aGBlxcXFBSUgKpVApPT88aP59YLOaNfVfu5QJAfHw8nJycAKDa5wSAJ0+eoGXLlhCLxTA0NISJiQmvbPn6kZGRKC0t5fXePTw8EBQUhK1bt8Lf3x+pqam1tgXzfmOBmeGYmJggLi6OtywiIgKnT5/GlClTgIoAJB8KAIALFy5AQ0MDnTt3RnR0NAoKCqoNJ0RFRcHOzg4aGhrIz8/n9iV37tw5eHt7w8jICFFRUbzyRSIR1NXVYW9vj+TkZNjY2PD27e7uDqFQiNjYWJSUlPC2rapy3QsKCpCamlptX/LXVYN4WloaIiIi0LNnT0ilUt57CQkJyM7O5nrMYWFhcHBw4May5T755BP8999/KCwsxA8//FBLSzDvOxaYGc7w4cPx008/4ejRo4iMjMSBAwfwySefYNCgQRg1ahTy8/ORlpaGFy9e4MmTJzh37hzmzZuHhQsXQk9PD9nZ2UDFkEJ8fDyICKgSEJ2cnKCuro6QkBCUlpbi0qVLCA4OxtKlS7l1K/dURSIRnJycoKqqCnNzc+6L486dOwgODoa2tjYAIDc3F6gYG65J5XqIxWKoqKjA2dkZqBhqiI6O5r1fVlaGvLw83L17F6NHj0b37t3Rs2dPFBUV4fHjxygtLcWjR48wa9YsCIVCuLi4ABVXALm5uUhOTkZSUhIA4MCBA4iJicGjR4/w4sUL7kbhvXv30LZtW4SGhjZqWzJvN9WmrgCjPBYsWICSkhKsXLkS+fn5cHJywqJFizBmzBigIlhpa2vj+++/x/Tp02FlZYXFixcjMDAQAODr6ws/Pz+MHDkShoaGiIqK4rbz9/cHABgZGWHnzp1YvXo1Vq1ahdatW+PgwYPc0EZUVBS++OILrk6Ve9BLlizB5MmT4eXlBX19ffTt2xf37t0DALRr1w69e/fGp59+ihs3blT7bEVFRUhISODKioqKgoODA9TV1YGKWRjyHnd5eTliYmKgoaEBNzc36OrqYujQoVi8eDEAICAgAH/88QdcXFzg7OwMU1NT2Nvbo3nz5gCAIUOG4OjRo2jfvj369++PnTt34syZM1i6dCl0dXUxfPhwTJo0CQBQXFyM3NxcbtYJwwCAoLY39+7du9zNzW1ZbZeHzPsjODgYR48exaVLl5q6Kq9VTEwMOnbsiIcPH3LBlmEaS35+Pq5cuZI+fvz4Gie5s6EMpt7EYjF3c+xdJhaLYWlpyYIy02RYYGbqTSwWw9HRsamr8dpJJJL34nMyyouNMTP1dv78+aauwhuxcOHCpq4C855jPWaGYRglwwIzwzCMkmGBmWEYRsmwwMwwDKNkWGBmGIZRMiwwMwzDKBkWmBmGYZQMC8wMwzBKhgVmhmEYJcMCM8MwjJJhgZlhGEbJsMDMMAyjZFhgZhiGUTIsMDMMwygZFpgZhmGUDAvMDMMwSoYF5iaSl5eHzMzMpq4Gj0wmg5mZGQwMDGBgYIBHjx699n3evn2b21+HDh1e+/4qk0ql3NO5X5VEIuE+h/zJ201p69atXH3mzJnzzu5TbsKECcjLy2uUssaMGcN9jgMHDtS4zMHBgVsWHR0NAEhMTGyUOrDA3EA//fQT1q9f/0plZGRk4IsvvoCGhgYAYPDgwZgxY0a1/VhbW4OI6ixvw4YN6NGjxyvVCQASEhJQVFSEEydOQCqVwtjYGAcOHICtrS0sLCzQoUMHLFq0CFlZWdw2OTk5cHd3x+nTp+ss/9atWxg7dixvmUQigZaWFiQSCS5evFjr9o1x7CtbunQpwsPDG6Use3t7SCQSDBw4EC4uLvXeLiQkBJaWlpDJZNXeq2+7KjouEokEPXr0gEQiwcqVKwEAU6ZMgYWFBWxtbdGjRw/s3r0bRUVFDd4fAMyfPx/nzp2rc58N2f5l3b17F6dPn4ZIJGqU8rZt24Y//vgDALi2VLTs5s2bWL16NdTU1GBvbw8A2LFjB/77779XrgMLzA30/fffw9TU9KW3JyJMnDgRs2bNgp6eHgBAJBLB09OTt15ERATc3d0hENT6IHMAQJs2bdCnT5+XrpOcRCKBQCCAn58fjI2NAQD//vsvfHx88Pvvv2Pu3Lm4dOkS/P398fjxYwCAjo4O+vbty52YtTl69ChUVflPM5NIJHB2dkarVq2441GT+hx7RQFOkb/++guhoaGN9sespqaGVq1aITk5Ga6urvXeLjIyEq6urlBRqf6nWN92VXRcJBIJPDw80KpVK2hpaQEA/v77b0yZMgWnTp3C8OHDsX79egQEBDR4f+np6QgKCqr2ORXtsyHbV1ZeXl5nPeSWLFkCIkJkZGS9t6mNvr4+cnNzAYCro6JlrVq1Qnp6OhwcHLjz2t/fH9u2bXvlOrzzgTkyMhJubm7YsGEDvLy8YGZmhrFjx2Lfvn3w9PSEpaUlFi1axK2fkZGBKVOmwN7eHpaWlggICEB+fj4AoGPHjkhISMC3334LKysrZGdnAxUnfO/evWFmZgYHBwd89tlnKC0tVVifkydPoqCgAP7+/gCAhw8f4vHjx2jTpg1vvYiICC5YDxkyBBs2bMDgwYNhZmaG9u3bQywWAwBmzpyJzz//HIWFhdy258+fR9euXWFmZgY/Pz+uJyrv3f7vf/9Dx44dYWZmhoEDB+L58+dAxaW9paUltLW1ubJEIhG6dOkCT09PDBs2DGfPnsXjx49x5MgRpKenw9TUFMHBwbCxsQEAPHv2DPPnz4ezszPMzMy4S9o5c+bg8OHDOH/+PCwtLfHrr79y+6zcwywtLcX8+fPh4OAAW1tbrudV07Hv2bMnZs2ahSFDhsDa2hqpqal1nhMymQxLly7FkCFD8PTpU942NR3rEydOwNTUlBf4N2zYAHd3d5SVlXHlxsTENCgwK/pSVtSuaWlpGDFiBKysrODh4YGzZ8/WeFzKysoQFxfHO67Z2dnIzMxE37594e3tjenTp2P79u34888/cf/+fYXnUUJCAkaNGgUrKytYW1sjJCQEaWlp8PHxgYqKCj744AN88MEHAKBwn//88w8++OADmJubo0uXLoiNja1x+0uXLsHKygqbN2+Gr68vZs6cWa/jd/bsWURFRaFPnz68L9nazvVZs2Zh6NChvHI+/vhjTJgwgXstlUphYmICfX39OpdVbm8TExNcv369QV8sirwVgTkmJgY//vgjYmJiGrxts2bNkJGRgSdPnuDq1avYunUrzp49i+TkZFy/fh1z5szBnj17kJ+fj8ePH6Nfv34oKirCX3/9hbCwMNy/f597COmaNWugra2N5ORkpKSkwMjICDdu3EBAQACmTJmClJQULFmyBCKRCM2aNVNYn+3bt/N6KREREVBRUYGbmxu3rLy8HFFRUXB3dwcqxqMvXryI1atXIzw8HDKZDAcPHgQAfPfddzA3N0fr1q0BAL/++itmzJiBVatWITExESNGjMDkyZPx4sULaGhoID09Hf/++y9OnTqFCxcu4ObNmwgNDQUqejyV/7AKCwsRFxcHDw8Pbpm5uTkcHByQnJwMMzMzbN++HVZWVtDV1YVMJsPnn3+OsLAwhISEQCqVYtq0aQCAVatWQSgU4rfffkNqaioGDhyocJ8HDx7E77//jgsXLuDy5ctcD07RsZfJZIiOjoZUKsX+/fshFothbW1d5znx888/4+HDh9i0aROaN2/O62nVdKxdXV1RXFzMBfGSkhIEBwdjwoQJXG8pKSkJhYWFDRrKEIlEvOMrV7VdFy9eDFSMye/Zswdt27at8bjEx8ejpKSEVw/5eSYvDwA3bJGcnFxtf5mZmejfvz/09PRw7do1hIWFoWvXrjA3N8e0adPQs2dPpKam4q+//gKAavskIowZMwZ9+/ZFZGQk5s6dCzs7uxq3l0gkePHiBaysrHDnzh1s3LixzmNXUlKClStXYtq0aejQoQOvHWs7111dXREbG8trg5s3b2Ly5MncsqrnZU3LqnYsZDIZCgsLuc7Oy1L6p2THxMTwbgrdunULTk5O9d4+LS0N6urqWL16NVRVVWFmZgahUIglS5ZAQ0MDbdu2hYqKCtTU1LB9+3Y8f/4cO3fuhJaWFm7evIm8vDzuZs7du3fh7e3NXXYSEebOnYuJEydy38BJSUncH01VUVFREIvF6NKlC7csMjISjo6OvEu/2NhYFBYWcj2phIQE7N27lwvU1tbWXB0KCgqQlpaG1q1bo7y8HIsWLcKCBQu4P7qhQ4di9erVSElJQUlJCQQCATZt2gQdHR2YmJigWbNmXFkSiQT9+vXj1be8vJzbr9yTJ0+gq6vLbSP/Yz5//jzu37+Pu3fvolWrVgDArRceHg6hUMgrKzc3F9nZ2bweR3l5OQoKCvDkyRO0b98eDg4OCo+9/Fi/ePECW7ZsgYGBQb3Oh+fPn2PdunWYPXs2DAwMYGtri8jISPTv37/WY+3o6AihUIi4uDhYW1vj1KlTKCgowBdffMGVLZVKgUqXunVJS0vD48ePFQbmyu0qPy6ZmZkoLy9H586dufUUHRf5kFTlm5AikQj29va88+zJkydARRtV3d93330HMzMz7Nixo9pw2t27d3l1ULRPIkJ5eTmSk5OhoaGBwYMH17q9WCzGhx9+iE8//RQAah0KkQsKCkJBQQGmT5+Oq1evIjo6GqWlpWjWrBkSEhJqPNddXFyQlpaGFy9eQFNTE3v37oWPjw98fX25sqVSKe/vVNGy/Px8pKen89pbfiX3zveYq95Uqs9NpsrEYjGcnZ25Xk1UVBQcHBy4G2/R0dGws7ODhoYGbty4wZ1clpaWmDlzJrZt28YF2rCwMPj4+HBli0QixMTE8P447969W2Ngvn79OrS0tHhfLJWHLCovU1NTg7OzM1JTU5Gfn8/rUcfFxXFliMViCIVCODs7QywWIz09Hd27d+fWzcnJAQC0bNkSUVFRsLKygo6ODgAgNTUVJSUlcHJyQmlpKRISEnjf/iKRCEZGRlyQlR+/9PR0tGvXjtu//I/52rVr8PPz460vFxYWBg8PD6ipqXHLJBIJUOlmCgBMnDgRkydPxpAhQzBx4kTu5lTVY49KMyGqfnHUZseOHVBRUcGkSZOAijvr8kvg2o61uro6bG1tuZ7W3r178emnn/K+EKRSKczMzLgvo7pERkZCKBTyerFyldsVFTef7Ozs4Ovrix9++IFbT9FxiY6OhrW1NTQ1NXn7qvy5UDF8IBQK4e3tXW1/165dw8cff1wtKMtkMoSHh9e5TxUVFZw6dQrR0dHw8fHhesY1bS+RSNCtW7d6HTdUfKls3rwZc+fOhY6ODhwcHFBSUsLNjqjtXHdxcQERISEhATk5Ofjll18wZcoU3meMjY2t1hOuukzRF3FSUhJUVFTq9cVSG6UPzEOGDKn1dV3EYjHvD1csFvNO0KioKN7rcePGITY2FlKpFGFhYRg+fDj33r1793hBNDk5GaqqqrC0tAQqeoC3b9+Gl5eXwrokJCTA3NwcQqGQWxYfHw9DQ0PeepcuXUKHDh2gpqaGqKgo6OrqwsLCAqj4lk5NTeX+mKOiorgvFvlYuImJCVfWuXPn4O3tDSMjI0RFRfGCgEgkgrq6Ouzt7REXF4fS0tJql7+Ve3OFhYWYM2cOnJyc0Lt3b+54yst89uwZmjdvrvCzh4WFVRtHl0gk0NHRgbm5ObdMKBRi0aJF+PPPP3H27FnuTnjVY1913/WRkZGBHTt24MmTJ3B1dYWNjQ3++OMP7hK4rmPt4uKCuLg43Lp1Cw8ePOBd+kLBZW1d5FdL8k5CZZXbFQAMDQ0RHByMDRs2YNmyZdyNKEXHRdEld2RkJK8tk5KSsGnTJnz++efQ19evtr+CggKFbRkdHY2CggKFbVl1n23atEFoaCi6d+/ODcUo2r6srAyxsbENasvNmzejoKAAa9asgY2NDT788EPuc8qPX03nuomJCfT09BAXF4eDBw9CX1+fG1pDxZS3oqIi3udRtEwqlUJTU5M3fHb79m24ubnxOiAvQ+kDs5OTE27duoUNGzY0eBgDChqoaiCu/Mfdrl07nDx5ErGxsSgqKsLVq1e59crKyvDkyROIxWJkZGQgLy8P5ubmKCsrQ0JCAkpLSzF37lyUlpbybp5VVlZWVq03ZWNjg1OnTuGPP/7A/fv3sWLFCpw7dw6zZ89WWH95707+LV25/vKeXUhICEpLS3Hp0iUEBwdzc3WrfimJRCI4OTlBVVUVEokEKioqvOMrEolgaGiIqKgonDhxAr1790ZaWhoOHToEVVVVZGVlIScnh9t/27ZtceXKFYSGhiIzMxMnT57kysrOzkZiYiIyMzORnp4OVJqRIVdcXIydO3ciIyMD6enpkMlksLW1VXjsUWUYBRV3+52dnXH48GGFx3/t2rWwsbFBamoqkpKSkJSUhC1btiAlJQV5eXl1HmtPT09cv34dW7duRY8ePRSOQVbuPfXp0wdffvmlwrrIy7e0tERMTAz3Tz4PtnK7RkdH4/jx48jNzUVGRgZatmyJFi1a1HpcKtftxYsXiI+Ph5aWFu7fv49du3ahd+/eaN26NdatW1dtfwDg4+ODAwcOQCKRIDk5GRcuXODaEQAePHiA+Ph4bjpn1X3Kx3Rzc3Px9OlT2NnZ1bh9fHw8iouLecdu+fLl8PPzU3gTPSEhAfv370dQUBCSk5O5trS0tOTarLZzHRVfGr/++iuCg4Mxbtw43j0h+bBM5c9T0zInJyfe0OatW7fwySef1Njm9aX0gRkVAWfixIkNDsryb2J5A8lvFslfExGkUin3et68efDw8MDAgQPh6+uLy5cvc2Wpqqpi4sSJ+P7779GxY0fEx8dzd7cHDBiArl27QlNTE0ZGRtzlVFWmpqa8y0sA2LJlC7y9vTF58mR88sknuH//Pk6ePMmNESsKFubm5mjRogVQcQLKT2gjIyPs3LkTe/fuha2tLTZv3oyDBw9yQxuKvqTkr6VSKWxsbLhekkwmg1gsxvHjx9G7d29899136N+/P65fvw5HR0du32pqatw48NixY/HZZ59hypQpaN++Pc6cOcPta9y4cbhz5w58fHy4+atV72jHxsbi8OHD8PHxwYIFC7Bjxw54enoqPPZQEAhbtmwJU1NTJCcnVzv2UVFROHr0KFasWMEbj5VP8xOJRHUe62HDhiE3NxehoaG8S19UjCnGxcXx6uPp6cnVVRGRSITLly+jQ4cO3D95uZXbNTw8HCtXrkSbNm3w559/ctMOFR2X4uJiJCQk8OoRFRUFmUyGb775BoMHD8a5c+fw7bff4vTp09z5WHl/qPgSs7KyQp8+fdCrVy/ust3X1xd+fn4YOXIkPv74Y6DiC7XqPv/++28EBATA19cXGhoa2LJlS43bSyQSmJiY8IaFvLy8kJCQoHCsdsWKFWjTpg0GDRrEW25vb19jj7nq6zFjxuD8+fN48uQJxowZwytHKpXCwsKCGwapbVnlz/zff/+huLgYgYGBNbZ5fdU6SXbv3r3L3dzcljXkEoOp2Z07d/D111/j+vXrTV2VakaPHg0iqrG3+TrY2dlh3rx5mDp1aqOUV1paiiFDhmDbtm3cl0dj+/bbb3Hx4kXcuXOHN/4aGxsLPz8/hIaGcvcYFi1aBC8vL+6G1psgEonQrVs3XLt2TeFNxbdln+fPn8cff/yB77//vlHKU8Tf3x/u7u7V9jFhwgQUFBQgJCSk1mWurq6YNm0a9+OwsWPHomvXrhg3blyt+83Pz8eVK1fSx48fb17TOm9Fj/ld4evrC319fe5uuDKRSCSwsLBAVlbWK99Rro/09HQ8ffoUrVq14m5QvqrNmzdj0aJFryUo37lzB5s3b8a+ffuwYMECLijLZDJkZWVxgVo+NHPmzBnY2dm90aCMSpfcLVq04O45vG37jIuLw/Xr17Fp06ZGqV9lMpkMly9fxtSpUyGRSPDVV19x7+Xn5yMrK4t39aBoWW5uLuLi4pCVlcUt+/3336GtrV1nUK4vFpjfsF27djVaIGosRUVFSEpKwr59++Dq6opnz5699n3KL40nTJhQbbL/y1q4cCE6derUKGVVtXTpUoSEhGDVqlW8G8IPHjyAq6srvvzyS1hbW3N34wcPHsz7wcKbIpVKQUTw8vLChg0b3sp9Ojg4YP369VBXV2+U+lVWWFiIcePGIS4uDkePHoWtrS333qRJk+Dq6soboqi6jIjQtm1btG/fHqh0/yEvLw/bt29vtHqyoQyGYZg3iA1lMAzDvIVYYGYYhlEyLDC/p8rKymBiYoK7d+82dVVeSkxMTL1Soiq7J0+ewMDAAGFhYQDLif1KFOXE/vHHH7ll8htzjx49euVcFq8bC8y1yMzMREBAANLS0l7bPhTlpW2s/Mq1SUhIQElJyWubVvY63bx5E+vXr+dmRpSUlGDr1q3w9fWFqakp2rZtiw0bNtSY4a+qmtp54MCBvMyDr8ODBw+gqqrKzaVX9pzYirZ/Fa87J/bIkSO5edLyZWVlZdXmLisbpU9i1JSuX7+Oe/fu8X4yXFV5eTnvJ9YNIc9LK8/bINemTZvXPmUtOjoaRkZG3I8nMjMzeT/lbkw1HaPy8nKUl5c36OerWVlZmD17Npfxr7y8HAEBARCJRFi+fDlcXFxw79497ifACxYsqLPMmtrZ398fVlZW9a7by4iIiICLiwv3U+jKObHlvyCV58ReunQpYmJisH79epw9exbXr1+HgYFBo+bEfpntq5LJZArzS1clz4ldVx7u+qqcE9vPzw8AoK2tDSJCZmYm7PesvAAAIABJREFUN4PCzMwMWVlZuH//Pry9vRtl343tvegx15RjuaCgAI6Ojtw0l9u3b8PY2Bg///wzTp48ienTpyM3N5eXs7mmvLHJyckICAjgctf27t2bS1PakLy2jZlfuTZSqZT7xd7FixfRtWtX5OfnY9y4cZg3bx633q5du7jcyQAwffp0LkdETXmonz17hpYtW3I9f/nUorS0NIwZMwb29vZwdnbGoEGDsHfv3hpzMCuydu1a9O7dm0vkv3fvXvz99984ffo0Pv30U3h6emLMmDEYOXIkV+958+Zh3LhxGDFiBKytreHr68sF9pra2cfHBytXruSmv8lkMmzduhWenp6wsLBA//79ufa9fPkyunfvjh07dsDNza1aju/azo3w8HBebhVlzold0/bKnhNbUbIhExMTXLlypc56NpW3IjC/Sj7m2nIs6+jo4KuvvsKePXuQlZWFiRMnYuzYsRg1ahSGDRsGb29vfPvtt0hNTcXatWuBGvLGZmdnY8CAAdylVGhoKMLCwqCqqtrgvLaNmV+5NtHR0XBwcMCVK1cwZcoU7N27F7q6uvDw8OCS8D969AgbNmzgyktMTMTp06exYMGCWvNQy+e1ZmdnIzQ0FH///TdkMhkCAgKgqamJiIgI7Nq1C//88w/8/f1rzMFcVVZWFkJCQjB69Gig4if1O3fuxMiRI6s9Z8/MzIybL56dnY34+HgsWbIE9+/fh5+fH6ZOnYri4uIa2/n48eMAwMuHfPbsWZw8eRJSqRR6enqYP38+UOnn60SEf//9F99++y327NnDpTWt6dxARY+5ckIfZc6JrWj7tyEntlQqhYaGBm/OskwmQ0pKSp31bCpKH5jl+ZgXLFiADh06NDg479y5k8uxbGVlhdjYWF6O5XHjxkEoFKJXr16wtLTE6tWrgYpeQ2RkZLX0hIryxq5btw4WFhZYsWIFtLS0kJKSAn19fdjZ2fHy2tra2sLQ0BBmZmZARYrQquXXll9ZXV0dQ4cORUFBAVJSUng5Z01NTeHp6cnLr1wbqVSKlJQUjB8/HkFBQdwTVdzd3bnAvHz5cmhqanI9iy1btuDTTz+Fra0tLw+1qqoqLw+1RCKBoaEh1q1bB1VVVWhpaSEhIQEPHjzAsmXLoKWlxSW1UVFR4eVgdnBwqPEG1JkzZ6Ctrc31fOLi4pCRkcHlXKgsJSWFuzSX99Td3d1hYGCACRMm4NmzZ0hLS6uxnaVSKXR0dLgkQ/v27cOuXbvg5OQEbW1tDBgwAFFRUUBFhkAPDw/MmDEDurq63OWxiopKredGfn4+EhMTqwXmqnkuXiYn9pEjR+Dl5QVdXV1uiONlcmJXbg9F21fNiV2fdJc15cSWS0hIwDfffAN3d3cYGxsrzIkNoN45saVSKbetXHZ2NoqLi+usa1NR+sD8qvmY68qxrKGhAV9fX6SlpWHz5s1clqnIyEiUlpYqTG9YOW8sEeH06dNcLw6VkpfjJfLaNlZ+5dqUl5cjPj4e165dg5WVFe9Go7u7O/Ly8nDmzBmcP38ex48fR1ZWFn777TecPXsW8+bNqzMPtUQiQadOnXgZu5o3bw6hUIicnByUlZVh5cqVcHR05BJUKcrBrKgtK48Jyo+FPE2nXGlpKf766y907twZRMRLXIVKCeL19fVrbOfKP8G9cuUKWrZsySsjNzeXS9daNZNZXFwcjIyMoKenV+u5ERkZyXt6zduQE1vR9sqeE7tqsiEiQkpKyis9u/N1U/rA/Kr5mFFHjuVjx44hNDQURkZGOHToELc8LCwMDg4OvB6AoryxeXl5yMvL410mXbhwgfvja2he28bKr1ybxMREFBcX49SpU8jKysLWrVu590xNTWFoaIiZM2di4cKF8PDwQNu2bfHll18iICAA5ubmdeahrpqpDBWX37t378ZXX30FFxcXpKSk4PDhwxAKhTXmYK4qPj6edzNOHnyqZnA7cOAAsrKyMG7cOCQnJ+PZs2fVUlK2a9cO+vr6CtsZVYJdfn5+tUB37tw5Lid1VFRUtV5u69at6zw3RCIR7OzsuPND2XNiK9r+bciJXXWZVCpFXl4ed+9DGSl9YH7VfMy15ViOiIjAnDlz8N133+Grr77C/v37uZSROTk5yM3N5fK9oiIAVM0bq6urCx0dHe7yateuXYiIiOBu3jQ0r21j5VeujXyMtEePHti+fTu2bNnCm7Lk7u4OBwcHTJw4EajIK1xWVsYlfKkrD3XVPMly3bt3R0pKCnr27Ik9e/bA0dGxxhzMipSXl3OzSADA1tYW7dq1w7Jly7j7BytWrMCSJUuwdu1aODo6QiwWQ0tLC1lZWcjIyMD333+PI0eOcENWitq5ajt4enoiNjYWd+/eRVFRETZv3oyHDx/iyy+/RHl5ebUeuUgkQuvWres8N+RPGpfPwFHmnNi1ba/MObGfPn2KjIwM3rJ///0Xenp63A13ZaT0gRmvkI8ZteRYfvz4Mb744gsEBgZi6NChGDNmDAwMDLg/2CFDhkBDQwPt27fHihUrgBryxqqoqGDbtm3YuHEjfH19ERoaCj8/Py4nc0Py2qIR8yvXJjo6mttHv379MHLkSEydOpW7ZG3Tpg22bdvGjcn16dMHEyZM4GZC1JaHOicnp9qYpZyenh4CAwORmpqKjh074ocffqgxB7MipqamvN6gQCBAcHAwHB0dMXbsWAwdOhQPHjxASEgI92MCsVgMIyMjDBs2DD4+Prhw4QJOnDjB9ZYUtXNRURESEhK4Y/nhhx9iypQp+Pzzz+Hq6orw8HCcP38exsbGiIuLQ1FRUbV2cHV1rfPcGDp0KJo1a8ZN6VPmnNg1ba/sObEVjTmfO3cOkyZNqvHKQhmwJEZMk5g1axby8/MRHBxc7222bduGR48ecU/dqI/x48fD2tq60X5d9jqxnNj105Cc2AcPHsTSpUuRnJwMgUAAiUSCUaNGcc/fbAr1SWLEfmDyDvrnn38wd+5che+1adMGu3fvfuN12rp1K1q3bg0PDw9IpVJcuXIFy5cvb1AZX3zxRYPz3YrFYvTq1auBtW0aEokEvXr1QlZWFgwNDV/6h0v1VTUndtVnT76M150T+9q1a9i3bx927tzJy4mdnZ3Ny4ldVFSEvLw8hIeHw9nZGQKBAEVFRVi+fDn+97//NVlQri8WmN9BnTp1wj///NPU1eAQEZ49e4a5c+fi6dOncHFxwZo1a6o9GqguLVu2xKxZs+q9fklJCeLj49+Kn51Xzom9b98+JCYm8sbTX4fKObE9PDxw7dq1Vy5z4cKFjVAzxZYuXYqcnByFObF79uwJVDxDU0tLCzt27OCukgICAoCKoZQVK1Y06IG5TYUNZTAMw7xBLB8zwzDMW4gFZoZhGCXDAjPDMIySYYGZYRhGybDAzDAMo2RYYGYYhlEyLDAzDMMoGRaYGYZhlAwLzAzDMEqGBWaGYRglwwIzwzCMkmGBmWEYRsmwwMwwDKNkWGBmGIZRMiwwMwzDKBkWmBmGYZQMC8wMwzBKhgVmhmEYJcMCM8MwjJJhgZlhGEbJ1PmU7KioKJlYLJa9meowzEuTdzKo4h/DKCUiEggEgpLa1qk1MGdkZKy2trZe3+g1Y5hGduvWrTCBQNBaIBC0a9++fVRT14dhaqOlpVVrZ1fw5qrCMK/PxIkTRQKBwK28vNxj//79oqauD8O8ijqHMhjmLbELgHGzZs0eNXVFGIZhGIZhGIZhmNeJjTEz74SJEydOEwgExioqKrv27NnDhjOYtxqbx8y8K6YBWFZaWmrc1BVhmFfFAjPDMIySYbMymHfFWJlMplVSUpLY1BVhGIZhGIZhGIZhXic2K4N5J0ycODEYgC0RjQ0KCmLDGcxbjY0xM+8KX4FA4CaTybSauiIM86rYrAyGYRglw3rMzLuC5cpgGIZhGIZhGIZ5L7BZGcw7gc3KYN4lbIyZeVewWRnMO4PNymAYhlEyrMfMvCvYrAyGYRiGYRiGYZj3ApuVwbwT2BNMmHcJC8wViMgMgF1T14N5Odu2bfuprKzMtkuXLoEdO3ZMaOr6MC/tjkAgKG7qSjQ1FpgrENFXALY2dT0Y5j1nJRAIUpu6Ek2NTZdjGIZRMiwwM8x7qqys7L3evzJjgbkOQUFBEAgEyMzMbOqqvDHJyclISkpq6mrUKiAgAC4uLk1djTfq9u3bKCoqapSy1q9fj6tXrzZKWZU1pI4SiQRff/11o9fhXcACM8MTHx8POzs73L17t6mrwlRy8OBB+Pn54dmzZ69cVmhoKI4cOYKePXs2St3kGlpHDw8PPHjwAEePHm3UerwLWGBmeEpLSyGTyZq6GkwVjdVTBoB58+YhMDAQAkHj3vt/mTqOHTsWixYtYsMajGJE9BUR0f3796lr166koaFBtra21K9fPwJAGRkZJPfTTz+Rq6srqampkbW1Na1evZrKy8u591+8eEELFy4kW1tbUlNTI0dHR1q1ahWVlZXR5cuXCQD9+++/VJmWlhYtWLCAiIi2bdtGXbp0oX379pG5uTmpq6tT+/bt6fLlyzRmzBjS09MjY2NjmjNnDpWVlXFlPH/+nGbPnk3Gxsako6ND7dq1o2PHjnHvb9u2jfz8/CgkJITs7e1JU1OTunTpQhKJhIiIEhMTCQDvX2BgIBERRUdHk7+/P2lpaZGFhQVNnjyZ95nrIz8/nw4cOEClpaUN2o6I6NixY+Ti4kLNmzendu3akbe3Nzk7O3Pvl5SU0MKFC8nMzIzU1NSoTZs2dPr0aV4ZycnJFBAQQEZGRtwxDQkJISKixYsXk7q6Om/9O3fuEAC6cOECERENGjSIFixYQDNmzCBdXV3S0dGhwYMH09WrV6lXr17UvHlzsrW1pUOHDvHKSUxMpCFDhpC2tjYZGRlR37596c6dO9z7gwYNovnz59PixYvJ2NiY9PT06PPPP6e8vDwiIgoODq7WLsHBwUREdP78eXJ3d6fmzZtT69at6Ycffqj1OMo/k0gk4i3/77//uPO+ZcuWNHbsWHr8+DH3fosWLah3797UqVMnat68OVlaWtK3337LtWVtdaxNTk4O7xgTkWVTxwJGiRDRV1KplHR1dcnOzo62bdtGO3bsIGNjY15gPnjwIAGgUaNG0S+//ELz5s0jgUBAq1evJiKisrIy8vf3p2bNmtHcuXPp0KFDtHDhQgoICCAiqndgBkAffPAB/fPPP3Ts2DHS0dEhADR58mS6e/curVixggBQUFAQERGVl5dTz549ubr//PPPNGHCBAJA+/fv55Xr6+tLf/75J126dIlsbGyoffv2RERUVFREP//8MwGgVatW0fXr1ykmJoaIiLp06UJGRka0b98+2rJlC3300Ud1/tFVlZ+fT6ampmRvb08HDx7kfanU5siRI9zxCAoKosWLF5NQKOQF5jFjxpCqqiotW7aMTpw4QYMGDSKBQEB///03ERGlp6eTqakpGRsb08aNG+ngwYM0fvx42rp1K1EDAjMAmjFjBoWFhdGqVasIADVr1ox27NhBt2/fpk8++YSEQiFJpVIiIsrIyCBTU1Pq1q0b7d+/nw4cOEA9evQgDQ0NLjgOGjSIhEIhff7553T79m06ePAgqamp0bx584iIKCsri+bOnUsA6LfffqPr169TVlYWFRQUcF9Uhw8fpgULFtDChQtrPZbffPMNNWvWjPflGBUVRZqamtS+fXs6evQobd++nfT09Khnz57cOi1atCBLS0s6fvw43bx5kxYuXEhCoZC+/PLLWutYH8bGxjRu3Dj5SxaYmf9DRF99/PHHpK+vT48ePeJOmt27d3OBWSaTkbm5OXXt2pV3Yo0fP550dHSooKCAjh07xguGVTUkMFeuR2BgIBkZGZFMJuOW2dnZ0WeffUZERMePHyc1NTVKS0vjlfvZZ5+Rp6cnr9zMzEzu/a1btxIAys3NJSIiiURCAOjEiRO8ciwsLKhPnz71+TurVVFREe3du5ccHR3JwcGB/ve//9UaoAsLC8nIyIi6devGW2/EiBFcYJZKpQSAlixZwr0vk8nIwcGBPvjgAyIimjJlCqmqqnIBs6r6BubWrVvz1rG2tqZhw4Zxr1NSUggA7dmzh4iIpk2bRl5eXrxAWFJSQlZWVjRz5kyuXFdXV17bDhw4kDw8PLjX8vMwOzubWxYXF0cAaO3atTUev6o6duxItra2vGUjR44kHR0devLkCbfs0KFDBICuXbtGVBGY58yZw9tu0qRJ1KxZM+7cUVTH+ujQoQPZ2dnJX7LAzMaY/09BQYHw4sWLGD16NIyMjLjlqqr/l4AvNjYWaWlp+OSTT3jb9u3bFwUFBYiJicGFCxfQvHlzBAYGvnKdmjdvzv2/hoYG1NTUeOOCFhYWyMnJAQD8/vvvKC0thZ2dHTQ0NLh/J06cwMOHD3nlamn9X8pia2trAEB6enqtdRk9ejQuXbqEGTNm4NGjun/xnJmZiaSkJCQlJSE3N5dbrq6ujkmTJkEqlWL16tX46quv8MUXX9RYzo0bN5CdnY3Zs2dDKBRyyyu3y7Vr1wCA1y4CgQB9+vThbmJeuHAB/v7+cHZ2rrPutancJqhoF3V1de61hYUFAPDaJTIyEtra2lyb6OjoIDU1ldcumpqavLa1traus03s7OzQqVMnrFmzBj/88AOKi+v+wVxqaip0dHR4y65evQp/f3/o6elxy/r27QsAtd4E7tevH0pLS3Hv3r0691sbbW1tPHz4EET0SuW8S1hgrpCQkNC8tLQUtra2Na6Tl5cHADA2NuYtNzAwAACkpaUhKysLZmZmvCDyuggEAu5kzszMhImJCcLDw3n/RCIR7ty5U2MZampqQD3mlK5Zswbbtm1DSEgI7OzssHPnzlrX/+yzz2BrawtbW1ssXryY9155eTlCQkKwZs0aGBoa4rPPPquxnJSUFAB46XYpKChAQUEBsrKyYGn5+jtj8uBauV0++uijau0iFotrPYZqamp1tolAIMCFCxcQGBiIuXPnwsnJCX///Xet2+Tm5vK+SFBx/Go7p2uir68P/P9OTa37rIuGhgZKSkoaZcbJu4LlY65gYWFRDKDW3qC8N1S5BwgAWVlZQMWJqqenx71WpLHvhMvp6+sjOzsbNjY20NDQeOXyqvZeBAIBZs+ejfHjx2Py5Mn48ssv4eXlhc6dOyvcfvXq1VyvUR5UZTIZgoKCsHHjRhARlixZgtGjR9f6JSa/eqlvu5iZmXHLs7KyoKamBk1NTejp6dU6F/11tktOTk6jzbmu2i66urrYuXMn5s6di0GDBmHQoEFITU2Ftra2wu0NDAyqzZ6wsLCo9ZyuibzHX/ULr6E938LCQjRr1qxaT/59xnrMFVq2bFnq6OiIEydOoKSkROE6pqamsLGxwe+//85bfvLkSWhpacHb2xv+/v549uwZjh07xltH3vuR90wqX6ZmZmbW6zK0Nr169UJZWRl2797NW/78+fMGlSMf5qh6GS3/Y9bR0cHKlSsBoNZL2C5dumDw4MEYPHgw2rRpAwB4/Pgx1qxZg/nz50MqlWLMmDF1Xll4eXlBKBTi559/rnGd9u3bQ0VFhdcuxcXF+O2339CxY0cIhUL4+/vjypUr1X44U7ldiouL8fjxY+69xviRTa9evfDPP/8gLCyMt7yx28XW1hYzZ87E06dPkZhY8yMPW7VqhRcvXvCWdezYEVevXkVhYSG37OTJk0BFOypCRAgODoa+vj5cXV1rrSMAnD9/Hr/++qvCsgoLC6v12N93rMdcybJlyxAQEIBOnTph7NixEAqF+O6773jrrFixAoGBgZgwYQL69u2LK1eu4PTp01i+fDm0tLQwevRo7Ny5E4GBgbh9+zbatGmDyMhIhIaG4t69e3BxcYG1tTVWr16NVq1aoaCgAIsWLXrlucMBAQHYu3cv5s2bh8TERLRt2xYPHjzA6dOnIZFIqo2N1sTCwgJ2dnbYsmULtLS08PjxY8ycORPDhw+Hrq4u+vTpwwVAHx+fBtVRX18fsbGx3PBJfVhaWmLs2LEICgpCYWEhPvzwQ2RkZOC3336DiYkJAMDe3h6BgYFYtmwZysrKYG9vj3379iErKwuHDx8GACxduhTnzp1Dx44dMXPmTJiYmODSpUvQ1tbGjz/+iF69ekFFRQWzZs3C7NmzIRKJsGDBggZ9PkWWL1+O8+fPo0+fPpgzZw6MjY1x8eJFlJWV4cyZM/Uup1OnTlBVVcWsWbMwbtw4FBYWYuzYsXB2dsaIESPg5uaGXbt2QU9PD/b29jWW4+Pjg59//hlExF0lLF68GMeOHUO/fv0wZcoUJCcnY8WKFfjggw/QvXt3btuQkBCYmZlBU1MTJ06cwJ9//omNGzdyAVlRHSdPnoysrCwMHDgQRISkpCRYWVnx6pSWlgZfX9+GH1zm3Sefx7xjxw6ysbHhpiEFBgZWm8e8e/ducnR0pGbNmpGNjQ1t2LCBd0c9NzeXJkyYQIaGhqSpqUlubm60atUqKioqIqq429++fXtq3rw5eXp60pkzZxTOyigoKODKnDx5Mpmbm/PuZnfv3p03pSkvL4+mTJlChoaGpK6uTu7u7rRu3ToqKSmpsdxz584RALp//z637Pbt2+Tm5kZaWlrk4uJCSUlJtHLlSnJwcCANDQ1ycHCgffv2NejO+6soLCykL7/8kgwMDKhFixb00UcfUffu3XnT5QoLC2n27NnUqlUrUlNTI29vb/rtt9945URERFC/fv1IS0uL9PX1qWPHjnTkyBHu/Z9++ons7e2pefPm1KdPH25qZOVZGT4+PrwynZ2dadSoUbxl8umGchKJhAYMGECampqkra1N3bp14816UVTunDlzqEWLFrxlBw4cIBMTE9LT0yN/f396/PgxjR07lkxNTUlTU5P8/Pzo+vXrtR7LX375hQDQw4cPecuvXr1KHTp0IHV1dWrZsiVNnDiRm0dNFbMy/Pz8yNPTk9TV1cne3p42bdpUrfyqdSQiKi4uJh8fH/L09KQXL17w1i8qKiIVFRXau3evfBGblcH8H3lgZph32bNnz6hVq1Z09OjRBm2naLpcY7hy5Qrp6OhUnmLHAjMbY2aY94uWlhaWLVuGkJCQpq4KAOD48eOYO3cuDA0Nm7oqSoUFZoZ5z0yaNAmqqqpNnqhKPs99/vz5TVoPZcSeYFKBiLoA+Kip66EkHAG0bOpKNERiYqJvaWmpppWV1V0NDY2GTXloejEAHtdjvUZTWFioIhaLtX18fPLf5H4rE4vFWiYmJsUGBgaVJ2yvEwgEeU1VJ2XBAjNTDREdBzC8qevxHhkhEAiON3UlGOXBhjIYhmGUDAvMDMMwSoYFZoZhGCXDAjPDMIySYYGZeePWrl0LgUAAgUCAqVOnNnV1moShoSF3DA4dOmTe1PVhGEbJEdFxIqJx48ZRv379FP5iq0ePHjRr1qw6f9k1ffp0+uWXX3jLRo4cSb169aKMjAx69uwZyWQysre3J3V1dTIxMaEePXpUeyzU20zRMcjIyKAtW7aQmpoaFRQU1Jz3lHkvsR4zU6Pw8HB4eXkpfK9v377o0KFDrdunpaVh586dcHd35y0XiUTw9vaGiYkJtLS0EBsbi/j4eBw7dgwnTpyAu7s7hgwZgvXr1zfq52kKNR0DExMTpKWlwdnZGdra2uzptwzD1I6IjpeWlpK6ujr3sNLK7O3tCQCdO3eOiIhSU1Opf//+pK2tTZaWlnTixAlKSUkhdXV1UlFRIS0tLWrbti0REZWWlpKamhodPHiQKy8kJIQ0NTV5D3edOnUqtWjRgnuc1OnTp8nT05M0NDTI2dmZfv31V27d2NhYGjhwIGlra5OOjg799NNPlJWVRQDo1q1b3HqBgYE0aNAgoopnBH7xxRfUo0cP0tbWJisrKwoKCqIBAwaQhoYGOTk5UUREBLft//73P3J3dyd1dXWysbHhjsv58+fJy8uLNm3aRGZmZqSlpcVdSdR0DOT69u1LI0eOJCL6tKnbnGEYJUdExyMiIggARUdHVwvMMTExBICSkpKIiGjo0KHUv39/Sk9Pp6tXr3LLFy5cSP379+dtKxaLCQDvKdHffPMN+fn58dY7deoU99zDkydPkr6+Pl2+fJmKiopozZo1pKOjQ8+fP6f09HRq1aoVBQYGUlxcHD169IgePnxIly9fJhUVFXr27BlXpre3Ny1dupSIiIYPH05mZmZ08+ZNys7OJm9vbzIzM6M7d+5QWloaWVpa0uzZs4mIaPPmzaSjo0OnTp2i/Px8WrduHdnY2BBVZOcTCoW0ceNGevr0KW3fvp333DtFx0DO3Nxc/hBfFpgZHjaUwSgUHh4ObW1tODg4VHsvKioKurq63PMCy8vLkZ6ejrKyMnTv3p1bfuvWLfj5+fG2FYlEEAgEaN26NW9fVYdMHj9+DIFAAC0tLcyePRvLly9Hr169oK6ujpEjR6KgoACJiYlYt24dLCwsEBwcDHt7exgZGcHc3ByRkZFwcHDgcgWXl5dDIpFwSftTU1MxdepUdOrUCYaGhtDW1sbYsWPRrl07mJmZwcbGBpqamnj69CmWLl2KxYsXY8iQIZDJZHjw4AE3NBEbGwsvLy/MmzcPLVq0QLt27QAAKioqNR4DVDzOKS0trdoQB8OABWamJuHh4fD09OQCTGWRkZG8gLJv3z44OjrCyckJmzZtAioeI3X37t1qQSkqKgq2trbQ1NTk7UseMOXOnz8PLy8vxMTE4OHDh+jVqxf3XnZ2NlDx2KnQ0FAMHTq02qOhIiMjeWVGR0ejqKgIbdq0ARFBJBLB09OzxvUlEgnc3d1x+/ZtvHjxAtu2bYO+vj5MTU0hk8lw4MABAEBERASvnJiYGBgbG8PAwKDGYyA/DgBYYGYUYk8wYRQKDw+Ht7e3wvciIiLg4eHBvTYyMsLx48cRFBSEiRMnYuzYscjMzERBQQHatm3L21YkEsHNzY17nZWVhczMTF6IPKtDAAAgAElEQVSP+cyZMzhz5gwOHTrEPWjV1NSUe/+XX35Bu3btYGxsjIKCAl6Ql4uKisKAAQO41//88w+0tbVhZ2eHxMREPHv2jAvEKSkpePr0KRdg09PTkZOTA09PT6SlpUEgECAlJQXPnz9HixYteF9WERERCAgI4F4/ePCAOzZisVjhMZDXT1NTs9aHzDLvL9ZjZhR68OABjIyMIJVKuX8ZGRlARe9SHnwkEgkOHz6MnJwcpKenw9DQEHp6etzDU8PCwhAbG8s9oLNqYL5//z5Q8TDU69ev4+uvv8ann36KOXPmYNSoUXB1dYW6ujoOHTqE0tJSnD9/Hrt37+ZmbLRv3x67du2CSCRCYmIi91y5wsJCrr737t3DsmXL4OnpCYFAgIiICLRo0YIbcomIiEDz5s3h6OjIvVZXV4ezszO8vLygrq6OtWvXQiaTISoqCnFxcUDF8IhYLOb1mCsH5pqOgfw4uLq6KrwiYRiGqUYikZwDQFX/LVy4kAoLC0koFNK1a9eIKh7HZG5uTpqamtSpUyduFsSLFy+oc+fOpKqqSqampiSTyaioqIiEQiEdPnyYuwG2bt06AkAqKipkZGREH3/8MV26dIl3k+zo0aNkZ2fHPT6p8vspKSnUt29f0tLSIkNDQ1q7di1XLz09PTI3N6dhw4ZR+/btacqUKUREtHLlSurSpQtXxtq1a6ldu3bc640bN5KXlxf3OiQkhBwcHEhdXZ1cXFxIJBLxbmRmZWVx67Zs2ZL2799f4zGQ69mzJwUGBspfspt/DMPUTv4Dk8YWHh5e7fmC7ysTExPauHGj/CULzAwPG2Nm3hj5jAw9PT3k5eWhRYsWTV2lNy4nJwePHz9GZmYmu/HH1IgNcDFvTFRUFIgItra2WL58eVNX540jItjZ2cHZ2RlgMzKYWrAnmDDVEJEHgFZNXY+GOHXq1MCioiK97t27/2pubv60qevTQJECgSCrqSvBMAzDMAzDMAzzdmBDGcw7YeLEib5EpFVcXHzn0KFDb9tTshmGh938Y94VwSoqKn+pqamxn9Ixbz0WmBmGYZQMm8fMvCvuEFGOQCBgwxgMwzAMwzAMwzDvNDYrg3knsFkZzLuE3fxj3hVsVgbzzmCBmWEYRsmwWRnMu4LNymDeGWyMmXnrTJo0iWp6j4i45//J/3/fvn3sPGfeKmwog3nrENEtAGWK3qv6UFYikr2pejFMY2GBmXnrENF8IqpPL7gMwNE3UCWGaVQsMDNvnaCgoOsCgeA+EZXXtE7FMIaKQCBY9GZrxzCvjgVm5m1V6yNQBAJBGRHt27dvX8obqxHDNBIWmJm30r59+84DCCOiamPNRAQiEpaXl29qmtoxzKthgZl5axHRt0QkVPBWMYBDBw4cSGiCajHMK2OBmXlrBQUFhQKIqjpDQyAQCMvLyxc2Xc0Y5tWwwMy8zUgoFM5DxfBFxX9LiejIgQMH0pu6cgzzslhgZt5qe/fuvUhEtyv1moUA1jRxtRjmlbDAzLz1iOgbAIKK3vKBH3/8Maap68Qwr4IFZuatFxQUdL1irFmooqKyqqnrwzCv6pVyCOzZs2eEQCA43HjVYRiGefupqqpeHD9+/Mcvvf2r7FwgEAhNTEyKOnfurP0q5TAMw7wr0tPTERYWpvsqZbxy2k8VFRVSUWEjIgzDMPj/MfHVy2iUmjAMwzCNhgVmhmEYJcMCM8MwjJJhgZlhGEbJsMDMMAyjZFhgZhiGUTIsMDMMwygZFpiZd97Dhw9x8OBB3Lt377WUf/fuXSxatAg3btyodT2RSIQffvgBOTk5r6UezLuDBWbmrVBSUoKzZ8+iqKiowdtu3boVX3/9NeLi4l65HllZWfD09MT48eO5ZT/99BP27NmDR48e1brttGnTsGzZMuTn579yPZh32yv/8o9h3oTu3bsjOjoaiYmJ0NDQaLJ6pKWl4eHDh022f+b9wAIz81YoKCho6ioAANq2bYsTJ07A0tKyqavCvMPYUMZ74Ouvv4aBgQE++eQTuLu7w8zMDF27dsXRo0e5dSIjI2FgYIDOnTtjypQpsLe3h6mpKW7evAkAiImJweeffw5ra2tYWFhg4MCB+O+//7jtd+/eDUNDQ/zwww/o2LEjzM3N4efnh927d+Obb76Bl5cXrK2tMWzYMCQlJXHb+fn5wcDAAKNHj4atrS2srKwwePBg3Llzh1vHx8cH6en//4Ektra2MDAwwPHjxxt8HPbv3w8nJyeF9Zd//g8//JC3jb+/PwwMDBATE4Nbt27BwMAAw4cPx4QJE2rdFxFhx44d8PHxgampKTp16qSwp33mzBl0794dpqamcHZ2xsyZM3lj0DY2NjAwMMC6devg4eEBY2NjbN68ucGfnXm7sMD8HgkPD0e3bt3QuXNnSCQSTJ8+HYcOHeKtI5FI8Oeff2LAgAHo1asXOnXqhJSUFPTr1w8XL16Era0t3N3dcePGDQwaNIh3Q00mk2HZsmWwtrZG9+7dERsbi8WLF+PgwYPo2LEjnJ2d8eeff/LGZ+WkUikGDBgAR0dH/P333xg4cCCkUikAoG/fvmjevDkA4OOPP8aQIUNgZWXV4M8fEREBGxsbtGzZEjdu3MDAgQPrvGFXmb6+Prp161avdRctWoSlS5ciNTUVzs7OePb/2LvzuKrq/I/jLzZBBUEEFRBBQRZZFFFxzcRc0nLNNndnLM1xbMJ0NJdJKXMvc6/EzDLLydJcRsXIpUYFle2yC4KgiECAC/v5/TFwflxExUS96uf5ePh4eM89y/ece3nf7/3ecz7n2jX++OMPrXk2bNjAxIkTSUxMpEOHDjRs2JBt27YxaNAgrl27pjXvqlWr6NatGz169OC1116rdZvF40mC+SkSGBjIunXr+P7779mwYQMAH3/8sdY8BgYG7N69m9WrV/PVV1+hp6fH0qVL+eOPP5gwYQIhISEcOHCAuXPnUlxczOLFi7WWf/nll/n222/55ptv6NOnD1SE1Pr169m3bx82NjacPXuWS5cuaS3373//mzVr1hAcHMy4ceMoKipi3bp1AHz44Yc0btwYgNWrV/PFF1/QpUuXe97/jz76iIMHDxIeHs67775LSUkJ8+bNq/Xyrq6ut+xvTWJjY9m4cSP169fn0KFDhISEcPbsWZydndV5rly5wvvvv4+ZmRm//vore/fuJSwsjJEjR5KQkHDLB+aSJUvYuHEjP/zwA3Z2dve45+JxI8H8FDE0/P+fFEaMGEG9evVITk4mJydHne7m5oabm5vWciEhIQBMmjRJnTZq1CgAfv/9d615q469Vv6/efPm6vZbtWoFFWc3VGVgYKD+/9VXXwUgLCzsPvb2VpW9biqGdwwMDAgPD7+lJ3u/Dh06BMDw4cNp164dVOxf1R8tjxw5QlFREZaWlnz55ZfMmzeP+fPnq2Pp1U/tGzZsWJ22Ueg2+fHvKaWnp0fjxo3JzMwkLy9PnW5qeus9D7KzswFo1qyZOs3KygqAGzduUFRUdE/bpcpdrWtiaWkJoNWuumZsbIy5uTk5OTm3DBvcr8uXLwPg4OBw23kqP5guXLjA2rVrb3m+6ocIt3ldxJNLgvkpVVhYqP7IZG5ufsdwatKkCRkZGWRlZalDCpXhY2pqirGxcZ22rfJHMnNz81ueKy8vr5NtXLt2jdzcXAwNDbGyslI/BOpi/ZUfYJXHqCaNGv3vBhfDhw/n888/v+9tiieLDGU8RSp7toqisGLFCsrKynB1dVV7qLfTo0cPAL788kt12saNGwHo2bNnnbbt+vXrrFmzBoBu3bqpz5uZmQGoF4kUFxff8zZu3LgBFfu/dOlSFEWhZ8+emJiYqN8AEhMT1eGEiIgIEhIS7nkfvLy8APj++++JiYlRt1n1m0Xlvu3fv19r2CIiIkJtp3h6SY/5KfLuu+/y5ZdfcvnyZTIyMtDT02Pu3Ll3XS4gIIC9e/eyfv16fvvtN/T09Dh37hwmJib885//rJO2Pffcc7Ru3ZqkpCTy8vKwsLBg2rRp6vOdOnUiLi6Ol19+GScnJ9q2bcvq1avvaRuzZ89m27ZtZGRkcPnyZerXr8+CBQugopfbtm1bNBoNXbp0wdbWlnPnzlFWVnbX9VYOMxw+fJjXXnuN3r174+fnx8mTJ+nVqxeurq7k5eVpnS7n6urKa6+9xvbt2+nfvz+enp6UlJQQFxfHwoULmTJlyj3tm3iySI/5KeLk5ERiYiK5ubl07tyZHTt2MGjQoLsu16ZNG37++Wd69+5NYmIi8fHx9OzZk927d6u9w/vl6Oio9i4HDRrEgQMHtE6JmzdvHv369aO0tJT4+Hi1h1tbHh4eDB48mKSkJK5du4a/vz/79u3D29tbnefzzz+nW7du5Ofnk5mZyfTp02/5IbQmQ4cOxcLCgsuXL6uXW3/11Ve88sormJqakpqaipub2y1nU6xevZq5c+fi4OBAdHQ0aWlpdO/eHU9Pz3vaN/Hk0bufhTdu3Pi6ra3thu7du5vVXZNEXXvnnXfYsmULGzZs4OWXX37UzdHi5+dHQkICkZGRchqYeCJkZGRw+vTpoxMmTOj1Z9chQxnisXT9+nXGjRt31/kmTJhQq28FQugSCWbxWCotLeXIkSN3na/yIhchHicSzE+BlStXsnLlykfdjBpVrVdxLyrPQRbiSSQ//gkhhI6RYBZCCB0jwSyEEDpGglkIIXSMBLMQQugYCWYh7iA3NxdLS8s7liAdPHiwWjtaiLogwSzuav369bRs2ZKmTZvi7u7OmDFj1LuLPGpbt27lo48+0pp28OBBnJycaNq0KU5OTgwfPpyzZ8/Wan3BwcF4enqSnJwMQHh4OIaGhnh4eNx2mdjY2Fpdui1EbUkwi7v6/fff6dmzJ0ePHuWrr74iPz9fLWZfXW2K/tSl1atXY2NjozUtNDSUpk2bEhoayr59+2jUqBHTp0+v1focHBzo16+fWnEvIiICNze3296Z+8qVK1y9ehV3d/d7avfDPk7i8SLBLO4qPDycAQMG4ObmRseOHRk9ejSpqank5+czf/58XnzxRaZMmYK7uzvffvstAHv37qVnz57Y2tri5+fHgQMHoKJ06JAhQ3jzzTdp06YNrVu3ZunSpcycOZNWrVrh4uLCzp07oeIGsO7u7ixbtgwfHx8cHR2ZOHGiWju6a9eunD9/nrlz59KyZUuysrKgIky7dOmCvb09DRs2JC8vTy0MFBAQwMiRI9V9S01NxdLSksjISL777js6d+7MuXPn1FrQ586do3379ur8+fn5zJgxAxcXF9q0aUNgYCAWFhbqh8Pp06cZOnQodnZ2ODs7s2jRIqio/9ykSROWLFnCs88+S+fOnR/KayceTxLM4o5yc3NJS0vD19eXsrIywsPDWbduHR07dqRRo0bExMQQGxvL1KlTiY6OZsSIEezevZtp06axaNEikpOTeeWVV3jzzTe5ceMGpaWlnDx5kmeeeYYzZ84wZMgQVqxYQadOnQgPD6dTp058+umnUNGrzMzMpEGDBoSEhLBjxw6Cg4PVWtAffPABpqamXLhwgdTUVKytrQGIioriyy+/xMrKSq0e98knnwCg0Wi0qrdFR0djZGSEq6srI0eO5OWXX6Zt27bq8xEREertocrKynj11VcJDQ3lm2++ITg4mLCwMHUY49SpUwwePJgePXoQFRXF119/zapVq0hLSyM2NhZFUcjKyuLw4cMcPXr0ob2G4vEjwSzu6Ny5c1BREL9Zs2YMGDAADw8Ptm3bBhV31Q4ICMDT0xN9fX2MjIyYM2cOs2bN4tlnn8XY2JgRI0ZQUFBAamoq6enpdO3alVGjRmFmZkbTpk3p0KEDI0eOpFGjRnh6eqq3VUpPT6dp06ZMnToVc3Nz/Pz86NatG/Hx8VAxZOHj44O+/v+/ja9evUpGRgYhISFcvHiRzZs389tvv/HFF1+o7a0ezG3atKFevXro6emh0WjUYM7Pzyc5OVkN5h9//JGIiAi++eYbOnbsSMuWLfHx8VGHMebNm0fPnj2ZMWMGpqamhIWF0bhxY5o1a0ZMTAxWVlYsXrwYQ0NDGjZs+JBeQfE4kloZ4o4qe7HffPMNhYWFNG/eXL1xal5eHhkZGfTq9f/VDTUazS3TKm9h1aRJE6Kjo7V6pNHR0VpBGRcXpwZdTEzMLT+65ebmqjd0DQsLw9fXV+v5iIgIDAwMcHV1xdjYmKFDh7J9+3ZOnjzJCy+8QH5+vtY6q26/tLSUhIQEdfuRkZHo6+ur8//yyy907twZW1tbreVff/11ioqKCA0NxcrKCgcHB0pKSvD29mbnzp3Uq1ePmJgYunXrhpGR0X29HuLpID1mcUfh4eG4urrSpEkT7OzstO5mrdFoMDIywtnZWZ1WWSi+8s7YAHv27MHHxwdra+sahxKqB2XlY41Go/WjWnp6OhEREWrFuDNnzmgVuqdiGMPJyUm9D6GiKFy+fBkLCwtiY2MxMjKiTZs26nOhoaFqexITEykqKlI/OKKiomjdurXag8/NzcXCwkLdVkZGBpGRkVpt/Oyzz4iMjCQlJYUDBw7g4+NT474IcScSzOKOwsPDb3sqWExMDG3atNHqBbq4uGBsbMyOHTsoKSnh4MGDBAUFMX/+fPLy8khPT1eDt3J4o/LxjRs3SE5O1grm0tJS8vLyCA0NZcyYMfTq1Ys+ffpQWlpKbm4uGo2GS5cuqTdTjYiIwN7enqysLEJDQ5k0aRJxcXGMHTuWmzdvUl5ezpUrVygqKuK9997Tao9Go6FJkybqzVQrq9dVnkHRtm1bjh07RmJiIleuXGHKlCmUl5fj5uaGsbExXl5erF+/nvz8fLKysjh9+rTWsar6TUGIO5FgFreVn5/PhQsXcHV1rfH5mnqB1tbWrF27lo0bN9KqVSuWL1/Oli1b6NWrFxqNBkNDQ3V9ledCV64jNjaW8vJyPDw8KCsrIz4+nrNnz+Lh4cHYsWPp3r07QUFBABgaGjJp0iRWr15N165dSUpKgorhh8pzkcePH09ZWRn79u3Dx8cHf39/OnXqhJ+fn1ad5qrBXDU8R4wYgZGREbNmzQJg2rRpdOnSBX9/f4YMGULr1q2xtrZWb3O1du1asrOz8fPzo3///qSkpEDFUE5WVpb0mEWtya2lhE6Kj4+na9euXLx4UR1KEOJxUBe3lpIes9BJGo0Ge3t7CWXxVJJgFjqpcvxaiKeRnC4ndNLs2bMfdROEeGSkxyyEEDpGglkIIXSMBLMQQugYCWYhhNAxEsxCCKFjJJiFEELHSDALIYSOkWAWQggdI8EshBA6RoJZCCF0jASzEELoGAlmIYTQMRLMQgihYySYhRBCx0gwCyGEjpFgFkIIHSPBLIQQOkaCWQghdIwEsxBC6BgJZiGE0DESzEIIoWMkmIUQQsdIMAshhI6RYBZCCB0jwSyeKD169GD16tWPuhlC3BcJZnGLH3/8kb59+2JnZ0fbtm2ZMmUKV65cqfXyM2fOZM+ePVrTlixZwrPPPvsAWvv/CgsLiY+Pp127dredZ/DgwcyZM+eBtgPg8uXLjB49mvT09Ae+LfHkkWAWWlatWsXkyZMZMGAA+/fvZ/ny5Zw8eZK//vWvtVo+IyODzz//HHd3d63p7dq1o1+/fg+o1f8THR1NaWnpHYPZ39+fjh07PtB2ABw7dowzZ85gZ2f3wLclnjwSzEJ15swZPvjgA5YtW0ZAQADe3t4MHDiQ+fPnc/z4cXJyctixYwd9+vQhICCANm3a4O7uzpIlSwBIT0/H19cXfX19evfuTe/evQH4+9//zuuvv87NmzfVbe3du5eePXtia2uLn58fBw4cAODq1at4enry5Zdf0rVrV2xtbRk8eDDXr18H4Pr168yZMwdXV1fs7Ozo1KmT2jsPDw/H0dERCwuLGvfP19eXhQsX0rBhQwAWL17MlClTmDZtGg4ODrRp04adO3cCEB8fj7u7O8uWLcPHxwdHR0cmTpzItWvXAOjSpQuffPKJuu5vvvmGVq1aAbBz506mTp1KdnY29vb2ag89KCiIdu3a0aJFC0aMGEFpaSkAb731Fp06daKwsLCOX1HxuJJgFqr169fj7OzMqFGjtKbb2toCkJWVRX5+PsnJyfTr14+wsDBmzZrFkiVLOHnyJHZ2drz11lv06dOHtLQ0fvnlFwA+/vhjdVgEYPfu3UybNo1FixaRnJzMK6+8wptvvsmNGzcwMTEhIyOD33//nR9++IH9+/dz4sQJDh8+jKIojBs3jtjYWEJCQkhKSqKkpIQ//vgDKoL5Tr3l7777DkBtx40bNwgODmbgwIFERUXxzDPPsHLlSgDKysrIzMykQYMGhISEsGPHDoKDg9m4cSNFRUUkJSXh6emprjs6OhoPDw8AXnrpJXx8fJg7dy5paWl8+OGHxMTEEBAQwIoVKzh79ix/+ctfMDQ0BMDMzAxzc3MMDAzq8NUUjzMJZqE6fvw4gwYNQl9f+22RmpoKQLNmzUhPT6d3797079+fRo0aMX78eMzMzIiPjwcgNDQUX19freULCgpIT0+nbdu2lJWVMWfOHGbNmsWzzz6LsbExI0aMoKCggNTUVM6fP4+enh7Lli3DxsYGb29vjIyM0NfXZ9euXZw6dYovvvgCGxsbysrKSE9Pp0OHDlARzO3bt7/t/sXGxmJmZoa9vT0A58+f55VXXuH555/HzMwMDw8Pdd/T09Np2rQpU6dOxdzcHD8/P7p160Z8fDxxcXGUlZWpQQyg0WjUoC4pKSEyMlLrOFT2jlNSUrCysmLgwIHqc0uWLOHw4cMYGRn96ddOPFkkmIUqKyuLFi1a3DL94MGDeHh4YGFhQUxMjFYgFRcXc/36dSwtLSkvL+fcuXO3BLNGo8HAwABXV1c0Gg0ZGRn06tVLff7q1asANGnShOjoaFq2bImZmRkAaWlpFBcX4+Liwg8//MALL7xA48aNATh79iwmJia4ublRXFxMbGws3t7et90/jUajNfZdtZcLkJSUhIuLC8At+wmQm5tLkyZN0Gg0WFlZ0bx5c611V84fGRlJSUmJVu/dy8uLzz//nJUrV+Lv709aWtodXwvxdJNgFqrmzZuTmJioNS0iIoJdu3YxefJkqAigyqEAgP3792NiYkL37t2Ji4ujoKDgluGE6OhoWrdujYmJCfn5+eq2Ku3ZswcfHx+sra2Jjo7WWn9UVBTGxsY4OTlx4cIFHB0dtbbt6emJgYEBCQkJFBcXay1bXdW2FxQUkJaWdsu2Kh9XD/H09HQiIiLo06cPsbGxWs+dP3+erKwstcccFhaGs7OzOpZdafjw4Zw8eZKbN2/y6aef3uGVEE87CWahGjlyJFu3bmX79u1ERkayefNmhg8fzpAhQxg1ahT5+fmkp6dz48YNcnNz2bNnD++++y6zZ8/GwsKCrKwsqBhSSEpKQlEUqBaILi4uGBsbs2PHDkpKSjh48CBBQUHMnz9fnbdqTzUqKgoXFxcMDQ2xs7NTPzhOnz5NUFAQpqamAGRnZ0PF2PDtVG2HRqNBX18fV1dXqBhqiIuL03q+tLSUvLw8QkNDGTNmDL169aJPnz4UFhaSk5NDSUkJV65cYfr06RgYGODm5gYV3wCys7O5cOECKSkpAGzevJn4+HiuXLnCjRs31B8Kz5w5Q4cOHTh8+HCdvpbi8Wb4qBsgdMesWbMoLi5m4cKF5Ofn4+Liwpw5cxg/fjxUhJWpqSmrV69m6tSptGzZkvfee49x48YB0KlTJ/z8/HjttdewsrIiOjpaXc7f3x8Aa2tr1q5dS2BgIIsWLaJt27Zs2bJFHdqIjo5m7Nixapuq9qDnzZvHm2++Sfv27WncuDH9+/fnzJkzAHTs2JG+ffvy8ssvc/z48Vv2rbCwkPPnz6vrio6OxtnZGWNjY6g4C6Oyx11WVkZ8fDwmJiZ4eHjQqFEjRowYwXvvvQfA6NGj+c9//oObmxuurq7Y2Njg5ORE/fr1ARg2bBjbt2+nc+fODBw4kLVr1/Ljjz8yf/58GjVqxMiRI3njjTcAKCoqIjs7Wz3rRAgAvftZeOPGja/b2tpu6N69u1ndNUnoqqCgILZv387BgwcfdVMeqPj4eLp27crFixfVsBWitjIyMjh9+vTRCRMm9KrF7DWSoQxRaxqNRv1x7Emm0Wiwt7eXUBaPjASzqDWNRkObNm0edTMeuJiYmKdiP4XukjFmUWt79+591E14KGbPnv2omyCectJjFkIIHSPBLIQQOkaCWQghdIwEsxBC6BgJZiGE0DESzEIIoWMkmIUQQsdIMAshhI6RYBZCCB0jwSyEEDpGglkIIXSMBLMQQugYCWYhhNAxEsxCCKFjJJiFEELHSDALIYSOkWDWAf/85z95+eWXH/p24+Pj1TtZP8727NmDvb095eXlAJSXl2Nra4ulpSWWlpZcuXKlTrbj7OysrjMuLg6A5OTkOlm3EFVJMFfYunUrH3300SPZdnh4OO3bt6/VvJcvX2b06NGkp6ff1zZPnDjBRx99hJ7e/+7Hm5WVxT/+8Q/c3d1p0aIFzzzzDLt27ar1+mpq19WrV/H09Lyn9fwZEREReHl5oa//v7fz+fPnKSws5Pvvvyc2NpamTZvedtn//ve/TJgwoVbbOXHiBIGBgdSrVw8nJycA1qxZw8mTJ+toT4T4HwnmCqtXr8bGxuahb7e8vJzo6GjatWtXq/mPHTvGmTNnsLOz+9PbzMzM5O2331Y/iC5dukTfvn2Jjo5mzZo17N69m2eeeYa//vWvHD9+/E+3y8zMjP79+6sh9qBU/2CLiYlBT08PPz+/O4YywPbt2zE0vPMd1ip74s2aNSMjIwNnZ2d1GX9/f1atWlUn+yFEJQlmoGvXrpw/f565c+fSsmVLsrKyADh9+jRDhw7Fzs4OZ2dnFi1aBEBkZCQeHh4sWbKE9u3bY2try4QJE9i0aRPe3t7Y29szZ84cqBgucHd3Z9W5hOMAACAASURBVNmyZfj4+ODo6MjEiRO5du0aAAkJCVy7dk0rWI4ePUrfvn2xtbXF2dmZV199lZKSEnbu3MnUqVPJzs7W2kZxcTGBgYF4eXlhY2ODv78/Go3mtvv74Ycf0rdvXzW0AgICMDY25scff6RPnz506NCBwMBAXFxc2LNnDwA9e/Zkzpw5+Pv7Y29vz4ABA9Sv8zW1KyMjAxsbG4KCgnB0dISKgFu5ciXe3t60aNGCgQMHEh8fD8DixYuZMmUK06ZNw8HBgTZt2rBz5867HhMqesxVP9hiY2Oxt7fH1NQUgJKSEmbOnImzszOtWrVi4cKF6n5v27aNvXv3Ym9vz+7duwHo06cP06dPZ9iwYTg4OJCWlqa1bnd3d/Vx8+bNOXbsmNoWIerCExHMM2bMwNnZmZdffvlPfcX/4IMPMDU15cKFC6SmpmJtbc2pU6cYPHgwPXr0ICoqiq+//ppVq1aRlpaGkZERly5dIjc3l5CQEFauXMlPP/3EhQsXOHbsGAEBAWzYsIH8/HzKysrIzMykQYMGhISEsGPHDoKDg9m4cSNUhIqVlZXa0zx+/DijR49m8uTJpKamMm/ePKKiojAyMuKll17Cx8eHuXPnkpaWxocffgjA+PHj2bdvH1u3biU+Pp7mzZuzbt26Gvc1MzOTHTt2MGbMGKj4YDhw4AAzZ86kQYMGWvPa2tqqH1KXLl3i6tWrbNu2jaNHj1JUVKTetLSmdtna2vLJJ5/QsmVLGjVqBMB7773HTz/9xM6dO4mNjcXCwoKZM2cCcOPGDYKDgxk4cCBRUVE888wzrFy58q7HJCMjgytXrmgFc0xMDG5uburjLVu2sG/fPvbv38+hQ4fo168fAIsWLcLAwICff/6ZtLQ0Bg8eTHl5OXFxccTGxvLFF1+g0WhwcHBQ1xUbG6u17vLycm7evFln49hC8CQEc2RkJJs3byYnJ4fDhw/z/fff3/M6QkND8fHxUccoAebNm0fPnj2ZMWMGpqamhIWF0bhxY5o1a0Z6ejrGxsYEBgZiYWGBra0tBgYGzJs3D3Nzczp06IC+vj716tUjPT2dpk2bMnXqVMzNzfHz86Nbt25qT/HcuXNqqCiKwowZM5g0aRIjRozA0NCQlJQUOnToABU9v8jISHx9fdV2HjlyhAMHDrB69Wp8fHzIyckhOTlZq1dX1Y8//oipqan6/IkTJ9DX12fgwIG3zJuamkrz5s25ceMGubm5zJgxA1tbW1q1asVLL72k7kNN7aIiINu2bQsV3xw2bdrEunXrcHFxwdTUlEGDBhEdHQ0V48KvvPIKzz//PGZmZnh4eKCvr3/XYxIeHk7Dhg1xcXHR2m7V/S8rK6OgoIDc3FycnZ3p0qWLeuwNDAzw9PRU501JSeHGjRusWLECS0tLGjZsqD6Xn59PRkaG1rorP7iKioru8A4T4t7ceXDtKREWFqYVKkVFRYSGhmJlZYWDgwMlJSV4e3uzc+dO6tWrh0ajwdXVVR1njI6OxtnZGRMTEwDi4uJo3bo1JiYmxMTE4OHhobW93NxcWrVqBRUfLJ07dwYgKiqK+Ph4duzYoc4bGhpKnz591HlLSkq0eocnTpzA1NSUESNGoKenh4GBAePGjWPKlCk17uvx48fx8fFRH2dlZWFlZUX9+vW15ktISCA5OZlu3boRGxuLsbGx1lhxbm4ulpaWt20XgEajoWPHjgAEBwfTpEkTrWORnZ2NlZWVegxffPFF9bmkpCRcXFzuekwiIiLUEKfiQ+L8+fNavdpJkyaRk5PDsGHDGDhwIJ9++ikmJiaEhYXh5eVFvXr11HljYmKwtLTUCutKsbGxAFrBnJKSgr6+Ps2aNavxeAvxZzz2PWYvLy8mTpyIpaUlzz33HCNHjrzndZw5cwZvb+9bpn/22WdERkaSkpLCgQMH1EDTaDRaf7gajUYrcKKjo9XHGo1G6w85PT2diIgINViioqLUXuWFCxcwNDTE3t4eKoLr1KlT6vhzWFgYzs7OWr24ymOQlJREREQESUlJzJ8/X6v3X1VSUhItW7ZUHzdv3pzs7Gz++OMPrfkCAwNxcHCgf//+6geRgYEBVHx9P3DgAM8999wd26XRaNR9y8/PvyW89uzZQ9++fSkoKCAtLU2dt+pxudsxiY6O1louMTGRkpISrWA2MDBgzpw5HDlyhJ9++on//Oc/artr+jCpur6qYmNjadCggdbQxqlTp/Dw8Lhl34W4H499MAMsX76cxMREvvvuu3s+W6G0tJTc3Fw0Gg2XLl0iLy8PY2NjvLy8WL9+Pfn5+WRlZXH69Gl1mephUDWIqfbHrdFoKC0tJS8vj9DQUMaMGUOvXr3o06cPJSUl6jg0gJ2dHaWlpZw/f56SkhJmzJhBSUmJ+iPW1atXyc7O5sKFC6SkpADQsWNHwsLCOHjwIOXl5YSEhFBYWHjb/S0rK8Pc3Fx9PGDAABo0aMBbb71FaGgox44dY+zYsYSEhLBp0yaMjIyIiYnByMiIq1evkpiYyFtvvUV+fj7Tpk27bbsyMzO5evWqehy8vb1JSEggNDSUwsJCli9fzsWLF/nb3/6GRqNBX18fV1dX9TWJi4ujbdu2dz0m2dnZ6vGjoserr6+vDm0UFRWxdu1aLl26REZGBuXl5eq3laysLJKTk7l8+TIZGRnq8rcL5piYGFxcXNQPPUVR+O9//8vw4cNr8U4TovaeiGC+H4aGhkyaNInVq1fTtWtXkpKSAFi7di3Z2dn4+fnRv39/NXBKS0tJSEhQg7jyx6LKx4qiEBsbi4eHB2VlZcTHx3P27Fk8PDwYO3Ys3bt3JygoCAAjIyOmT5/OzJkzycjIwMfHh6lTpzJo0CB69uxJgwYNsLa2Vs9+GDZsGCYmJnTu3Jn3338fgOeff56pU6fyzjvv4OnpyeLFizE2Nr7t/trY2GgNW1hbW/Pdd99x5coVhgwZwqRJk6hfvz7BwcHq8I5Go6GoqAg/Pz/69OlDUVER+/bto3Hjxrdtl0ajoV69ejg7O6vtnDx5Mq+//jru7u6cO3eOvXv30rRpU3UoqLLd8fHxFBcX07Zt27sekzfeeIPg4GD+/e9/Q0Wv1tHRUd3HhIQEtm3bhq+vL7NmzWLNmjXqt6OJEydy+vRpfH191bNPqo9PV1X9jIyTJ09SVFTEuHHj7uk9J8Td6N3Pwhs3bnzd1tZ2Q/fu3c3qrklPjvj4eLp27crFixdvGcN9VFatWsWVK1dYvHhxrZdxc3Nj7dq16vCLLhszZgyKorBt27Y6X7e7uztvvfWW+k1hwoQJ9OzZk4kTJ9b5tsTjKyMjg9OnTx+dMGFCrz+7jqe+x/wgaTQa7O3tdSaUAcaOHXvHc5yru3r1KleuXNE660GXxcTE0KJFCzIzM7WGOO5HdnY2iYmJZGZmqj3mffv2YWpqKqEsHggJ5gcoJiaGNm3aPOpmaGnSpAnTp0+v9fwajYb69evTokWLB9quulBYWEhKSgqbNm3C3d1dvYjnfiiKQocOHdQzZyqDOS8vj08++eS+1y9ETeR0uQeo8gIMXePv71/reZ955pn7rsvxsJiYmHD16tU6Xaeenh4XLly4Zfprr71Wp9sRoirpMQshhI6RYBZCCB0jwawDHlU95kfJwcGBkJCQGp+rvKowLCyszrebl5fH5cuX63y9j0KPHj1YvXq1+njEiBFqvegHXWr1UW6TivPTx44dW2frq6nW9qOsvy3BXOFpq8f8IAwePFiteHcnaWlpFBQU3PaH0fDwcAwNDW+5lP1+Xbp0ibFjx6qXzlNRO6Rv377Y2dnRtm1bpkyZck8FiWp63yxZsoRnn322TtteXWFhIfHx8bcUb5o5cyYxMTG88MILKIqCr68vNjY2uLu7M3jwYPbu3au1ntq+Zrd731Xf5r0u/2dt3LiRgwcP1llVv5pqbdc0berUqeTn59fJNu9EgrnC01SPua5V1iv29/dXa2PcSeWZHra2tjU+HxERgZubm1aA3i9FUZg0aRLTp0/HwsICKs7pnjx5MgMGDGD//v0sX76ckydP8te//rXW663pfdOuXTu1gt2DEh0dTWlpqfq++eOPP7h8+TK+vr40a9YMIyMjkpKSSE5O5vPPPycoKAh3d3fGjBnDxx9/rK6ntq9ZTe+7mrZ5L8tXV9vTG7Ozs1m5ciXFxcVqT/Z+1VRru6Zp7du3Z+vWrXWyzTuRYH4K6zFTUQjIysqKnJwcddqaNWvw8PCgoKCACxcuMHr0aFq2bImDgwN9+/ZVq8nVVK/Y19eXhQsXatWM+Oyzz+jYsSM2NjZ4eHioZUo1Gg1NmjThpZdeomXLlgwdOlSr5vG5c+e0jsed9u+3336jd+/e2NnZ0aNHDxISEmrc3507d1JQUKCekXLmzBk++OADli1bRkBAAN7e3gwcOJD58+dz/PhxcnJy2LFjB3369CEgIIA2bdrg7u7OkiVL7vi++fvf/87rr7/OzZs31fn27t1Lz549sbW1xc/PjwMHDkCVO7x8+eWXdO3aFVtbWwYPHsz169cBuH79OnPmzMHV1RU7Ozs6deqkXqEYHh6Oo6Oj+iETExMDFRcDVYqKiqJBgwY8//zzdOnShSVLljBx4kQ+/vhjysrKanzNfvnlF5577jm1lx0XF3fb911N2wwKCqJdu3a0aNGCESNGUFpaetvl58+fz4svvsiUKVNwd3fn22+/veN7ttKSJUtwdnbGwcGByMhIdfqhQ4fo1auX+j6uuq2OHTuqZWQBCgoKcHBwYMuWLeq06ld21jStefPmBAcH16qd9+OJCGapx1z7esyVOnTogKWlJUePHgUgJyeHFStW8MEHH1BYWMigQYNwcnIiJiaGw4cPExYWhqGh4W3rFX/33XcAap2JFStWsGbNGj777DMuXrxIjx491NPOYmJiMDAw4KOPPiIkJIRr166pV9NRQ+H72+2foiiMHz+e/v37ExkZyYwZM2jdunWN+/vJJ58wevRo9fH69etxdnZm1KhRWvNV9uKzsrLIz88nOTmZfv36ERYWxqxZs1iyZIl6K6ma3jcff/yxOiwCsHv3bqZNm8aiRYtITk7mlVde4c033+TGjRuYmJiQkZHB77//zg8//MD+/fs5ceIEhw8fRlEUxo0bR2xsLCEhISQlJVFSUqIWmwoPD79lGMPU1FTrfPOIiAjatm2rVdDq2WefJT8/n9zc3FtesyNHjjBq1CiGDx9OREQEP//8My1btrzt+676NmNiYggICGDFihWcPXuWv/zlLxgaGt5x+djYWKZOnUp0dDQjRoy443uWio7Mli1bWLBgAU5OTkRFRanPlZeXo9FoUBSF33//nblz57Jhwways7Nxd3cnMTFRnffrr7/GwMBA67ed6rW2a5pWVlZW4+mTde2xD2apx3xv9Zgr6evr4+/vz6+//grA0qVL8fHxYejQoSxevJgWLVrw/vvv07BhQ1JTU2ncuDGtW7e+bb3i2NhYzMzMsLe35+LFiyxdupRVq1bh4+ODgYEBycnJ6n5oNBrGjx9PmzZtaN26NW+99Ra//fabWtQpOTlZPSZ32j9FUdQ/FBMTE4YOHapWwKsqOjoajUZDjx491GnHjx9n0KBBt1ThS01NhYqvsenp6fTu3Zv+/fvTqFEjxo8fj5mZmfra1fS+KSgoID09nbZt21JWVsacOXOYNWsWzz77LMbGxowYMYKCggJSU1M5f/48enp6LFu2DBsbG7y9vTEyMkJfX59du3Zx6tQpvvjiC2xsbCgrKyM9PV2rDnX122m5ubmp93Ck4m+jevnS3Nxc9PT0MDMz03rNqOjBTpw4kbfeegtra2ucnJyoX7/+HettV91maWkpVJRCtbKyUmt832n5gIAAPD090dfXr9XQ1YIFC+jVqxc9e/bE2dlZq8eclJSEl5cX06ZNo1GjRmo1SH19fdzc3NRgLi8v57PPPmPs2LHqzSFqqrVd07SrV69SXFx813ber8c+mOvC7eoxh4eH4+DggIODA7t3767TesxNmjSBij+eyhCqrD1c9dfm0NBQ9Y/xbvWYHR0d8ff35/nnn79tPeaq+vXrx6+//kpiYiJff/01S5cuRVEUdu3apd7hhCoBxB3qFVctb7pnzx6aNWumDhsUFRURGRlJhw4dKCkpITExUavMqqIolJeXU15eTmRkJPr6+uoxu9P+6evr88MPPxAXF4evry+//PJLjft57NixW4rpZ2Vl1Xg148GDB/Hw8MDCwuKW1664uJjr16+rdairv28qj4OBgQGurq5oNBoyMjLo1ev/SyZUXgDTpEkToqOjadmyJWZm/ys1k5aWRnFxMS4uLvzwww+88MILaqGos2fPYmJigpubG8XFxcTGxmodw5p6e1FRUbe8TocOHcLLywtjY2Ot1ywrKwuNRsOQIUNuOSa3q7ddfZteXl58/vnnrFy5En9/f3V4qqbl8/Lybjk2d3P8+HEOHjzIggULAG7pMVcvv5uYmIi1tTWNGzfGzc1NHeY6ePAgqampTJo0SWtfqFZr+3b1tx/Gb1GPfTBLPeZ7q8dcVeUfz5QpU5g8eTLOzs7k5eWRl5enlsYE2L9/v9a+11QWs+r01NRUreWDg4PVmw3ExcVRXFys9Uf63Xff0a1bN4yNjYmKiqJ169Za9UXutH/t2rXj8OHD9OrVi/fee6/G/Tx//jx2dnZavenmzZtrfbWl4qv/rl27mDx5co37un//fkxMTOjevTvc5n0THR2tfihX/nrfvHlz9fk9e/bg4+ODtbX1LeVjo6Ki1BsSXLhwQb1XYuW2PT09MTAwICEhQa2+V6l6SF65coXMzEy8vLzUaXv37mXv3r1MnTr1lv2r/M2jprout3vf1fRhMHz4cE6ePMnNmzf59NNPb7u8RqPByMhIrT54N4qiMG/ePAwMDHjhhRdwdHRk4cKF/PHHH+oHQHR0tNbfWtXj6+bmRl5eHllZWWzcuJEXXnhB64fImmpt367+duXl+Q/SYx/MSD3me6rHXJWFhQUdO3YkKyuLd955B4BGjRphZmamhta6deuIiIhQ23C7esVV99nOzo60tDQKCwu5cuUK//rXvzAyMsLY2JjQ0FAMDAyIj4/n8uXLzJ49m99++40PPvgAKsa6qfIL/Z32r3JMtrLQ/+3Gl0tLS9X7DlYaOXIkW7duZfv27epw2PDhwxkyZAijRo0iPz+f9PR09bZae/bs4d1332X27NlYWFjU+L6pfhxcXFwwNjZmx44dlJSUcPDgQYKCgpg/f746b9X3TVRUFC4uLhgaGmJnZ6e+BqdPnyYoKEirBnXVY5SZmUl2drZaz5qKXmql33//nffee4+JEycydepUtfNSta329vZYW1uzdOlSMjIyiIyM5NSpU3CHetvVt7l582bi4+O5cuUKN27cUD+ca1q+so5M1TM5+vXrx9/+9rcaX8MdO3YQFxfHmTNnSElJISUlhRMnTqj7WlZWplV+l2qdHhcXF+rXr8/HH3/Mr7/+qn74Vqpea7umaXFxcWRnZzNs2LAa21iXnohgvh9PWz3m6vr168dHH32k9pT09fVZtWoVS5cupVOnThw+fBg/Pz+1DTXVKy4sLOT8+fPqH8HYsWNxcnLC19eXIUOG8Mwzz1BUVMSFCxc4cuQI06ZNY/r06fj6+hIXF8fevXvVnt2IESMwMjJi1qxZd92/o0ePMnr0aDp16oSJiQkrVqyocR9tbGxuudHsrFmzGDt2LAsXLmTAgAF89dVXzJkzh02bNkFFaJmamrJ69Wratm1LYGAg7733ntrbvN37puo3JGtra9auXcvGjRtp1aoVy5cvZ8uWLerX95o+4Csfz5s3D41GQ/v27Zk5cyb9+/dXx7Y7duxI37591R+uavrKXRnMzz//POPGjSM5OZlvv/1WvUN49dfM0NCQoKAgUlJS8PX1ZfTo0RQUFNz2fVd9mzdu3ODHH3/E39+fF198kWHDhvHGG2/cdvnq3ySpuJlC5XGs6ubNmwQGBjJlyhStjleLFi0wNjYmMjKSxMRECgsLbzmeldswMjJi/PjxrF+/nvbt2+Pn56e1jdqckbFnzx66detWq9ML75fUY36AdLEe89Po9OnTvPPOOxw7dqzWywQFBbF9+3YOHjz4QNtWFzZs2MBHH32kdh4e123OmTOH9u3bP7CrYLOzs/H09OSTTz65ZRvVa21Xn1ZUVETXrl358ssvtYaHalIX9ZilutwD9CjrMd+8efOOhe2PHTtW4xkMT6JOnTrRuHFjcnNz1R/T7kaj0TxWNahbt25NZmYmjRo1eijvt7re5o8//kjr1q0fSCjn5ORw9OhR1q1bh6Ojo9ZQRHZ2Nrm5uVq1tmuatmjRIgICAu4aynVFgvkBepT1mOvXr89vv/32SLati9atW8fVq1fvKZgf9NV7dSU2NpZz587h7u7Otm3b1NPUHqdtDh06tM7aVl1oaCjvvPMOfn5+fP755+q4dmWt7cohm8pTMKtPy8vLo0uXLne85LyuyVCGEELUIbm1lBBCPIEkmIUQQsdIMP8JU6ZM4e9///ujbkaNKk9jqj6t8tzZuvCwatIK8bSSYP4TYmJian3F0sP06aefcvjw4Vumz58/n3PnztXZdh5WTVohnlYSzPeovLychIQEtXC2rggJCWH//v289dZbWtN/+eUXDh8+rFVT4H49rJq0QjytJJhvU0OWipPofX19ad68OT179kRRFJKTk7l58ya//vornTt3xsHBQS1jSMVdMiZPnoyTkxP29vaMHj2a/Px8FEWhU6dOzJ8/nx49emBra0ufPn3YsWMHzz77LLa2tgwZMqRWtXirKysrY9asWUydOlXrktLy8nLmz5/PsGHDtGoKUHE11pIlSxg6dCi2trZ07txZLbpjaWmpXo5LRfGYJk2aqFf/PayatEI8rZ6IYL6fesy3qyH74YcfsmrVKgIDA4mLi+Ozzz5DT0+PmJgY9PX1sbGx4dChQ7z//vssX76c7OxscnJyGDBgAIWFhfzyyy+EhYVx9uxZ9u7di56eHnl5eYSFhbF9+3Z++eUXNBoN27Zt45tvvmHXrl0cO3aMX3/99a61eKvbt28feXl5DBgwQGv6119/zcWLF1m2bBn169fXqp+Ql5fHgQMHCAwM5Ny5c5SXl7NlyxZsbW0xNzfXKji/ceNGevfurdZFeFg1aYV4Wj32F5hUFqAB1HrMb7/9dq2Xr1pDtk+fPgwcOJDMzEw++eQTvvzySzXszM3NoeKHNFdXV/7xj39AxVVlVJSE/Pzzz7l+/Tpr166lYcOGnDhxgry8PFxdXbl58yY5OTnMmzcPe3t7iouLKSsrIyAgAFtbW5o2bYq+vj7169dXa/GGh4fTuHFjrl+/rlWLt7rvv/+eLl26aF3Jd/36dRYvXszbb7+NpaUlrVq1IjIyUr0Q4Pz582zcuFGtkufg4KD2tqvWrk1NTWX//v1ad5d4WDVphXhaPRE95vtRUw3Z33//HQMDgxqv/KpeozchIYFGjRphY2PD8ePH0dPTw9XVFXt7e/7+97+zatUqOnToQExMDOXl5WqRlbi4OEpLS9V1JSQkUF5ejru7+x1r8dbk+PHjalnOSmvWrEFfX18tJOPs7KyOM6elpZGfn39L7drKS5CrBvOmTZtwcnLSurz7YdWkFeJp9dgHc13UY65eQ7agoAATE5MaaxpXVo6rVL1m78SJE0lISCA2NpawsDCtEostW7ZUy09GR0fTrFkzrK2t1cdNmjShefPmd6zFW11lycuWLVuq0y5dusSaNWvIzc3F3d0dR0dH/vOf/6hDGdHR0TRq1EgtFJ+fn09aWppW7drExESuX7/Otm3beOONN7TujPGwatIK8bR67IOZ+6zHXFMN2Q4dOpCXl8fKlSvJysoiJCSEtLQ0SktLSUpKuiWYKx937NiRnTt3kpCQQGFhISEhITXORw01nKs+vlMt3uoqh2Kq1hv+8MMPcXR0JC0tTa1du2LFClJTU8nLy6uxQDtVSji2a9eOxMREFi1ahJ6eHq+++qo678OsSSvE0+qJCOY/63Y1ZD08PFi2bBlbtmzB29ubefPmYWhoSGJi4i13jqgauO+++y5eXl4MHjyYTp06cejQoRrno4aC81Wfv1Mt3uqsrKyoV6+eWm84Ojqa7du38/7772v1+CtP74uKiqoxmO3s7NRx9E6dOuHt7c2mTZsYM2aM1p0nHmZNWiGeVlLE6Anw0ksvMX78+DqtfnXgwAHGjBnDmTNn1Ftd3UtNWiGeVlLESEDFlXj//e9/62Rd58+fZ+vWrbzzzju8/vrraijzCGrSCvG0euxPlxPQu3fvOlvXV199xY4dOxg0aJB6GyIqznt+2DVphXhaSTA/IeoqnBcsWKDeHr4qc3NzCWUhHhIZyhBCCB0jwSyEEDpGgvkx8M9//vOB3TlY6jcLoXskmB8D4eHhtG/fvs7XK/WbhdBNEsw6rry8nOjoaNq1a1en65X6zULoLgnmispwgYGBeHl5YWNjg7+/PxqNhoKCAtq0acMnn3wCFTUimjZtytdffw3AhQsXGD16NC1btsTBwYG+ffsSHx9PZGQkHh4eLFmyhPbt22Nra8uECRPYtGkT3t7e2NvbM2fOHADi4+Nxd3dn2bJl+Pj44OjoyMSJE7l27RpUFDe6du2aVo/59OnTDB06FDs7O5ydnVm0aBEAJSUlzJw5E2dnZ1q1aqV1ultVUr9ZCN32RATz/dRjBhg/fjz79u1j69atxMfH07x5c9atW4eZmRn/+Mc/2LBhA5mZmUyaNIkJEyYwatQosrKyGDRoEE5OTsTExHD48GHCwsIwNDTEyMiIS5cukZubS0hICCtXruSnn37iwoULHDt2jICAADZs2EB+fj5lZWVkZmbSoEEDQkJC2LFjB8HBwWzcuBGAiIgIrKys1Bogp06dYvDgwfTo0YOoqCi+/vprVq1aRVpa5y8QJwAAIABJREFUGlu2bGHfvn3s37+fQ4cO1VgdD6nfLITOe+yDubIec05OjlqP+V4cOXKEAwcOsHr1anx8fMjJySE5OVkt6DNx4kQMDAx47rnnsLe3JzAwEIDFixfTokUL3n//fRo2bEhqaiqNGzemdevWpKenY2xsTGBgIBYWFtja2mJgYMC8efMwNzenQ4cO6OvrU69ePdLT02natClTp07F3NwcPz8/unXrptbGOHfunNYwxrx58+jZsyczZszA1NSUsLAwGjduTLNmzSgrK6OgoIDc3FycnZ3p0qVLjft8L/WbK50/f55//vOfeHp60rRp07vWb548ebK6rNRvFuLePPbBfL9OnDiBqakpI0aMwNHREX9/f55//nmmTJkCgImJCZ06dSI9PZ3ly5djZGSEoijs2rWLMWPGqOsJDQ1VayJrNBpcXV0xNPzf9TvR0dE4OztjYmICFRXaWrdujYmJyS31nQFyc3Np0qQJVHzwVAZzUVERoaGhhIeH4+DggIODA7t372bnzp3Uq1ePSZMm8eabbzJs2DAmTZpEYWFhjfss9ZuF0G2PfTDXRT1mLy8vkpKSiIiIICkpifnz56u9wW+//ZbDhw9jbW3NV199BRVf6/Py8mjVqpW6jv3792sFc+WdQbhLyU+NRqP2zgHS09OJiIhQgy0qKkqrEhzAZ599RmRkJCkpKRw4cEDdroGBAXPmzOHIkSP89NNP/Oc//7llX6V+sxC677EPZu6zHnPHjh0JCwvj4MGDlJeXExISovY0IyIiCAgI4OOPP+Yf//gHX3zxBRcuXKBRo0aYmZmpvcR169YRERGh1kyuXlazeu3lqsX1NRoNpaWl5OXlERoaypgxY+jVqxd9+vShpKREHYcGMDY2xsvLi/Xr15Ofn09WVhanT5+Git702rVruXTpEhkZGZSXl2t9cFSS+s1C6L4nIpjvx/PPP8/UqVN555138PT0ZPHixRgbG5OTk8PYsWMZN24cI0aMYPz48VhaWhIYGIi+vj6rVq1i6dKldOrUicOHD+Pn56feLiohIUEN4vLycuLi4tTHiqKod0EpKysjPj6es2fP4uHhwdixY+nevTtBQUEAGBkZMX36dGbOnElGRgYAa9euJTs7Gz8/P/r3709KSgpUnL2xbds2fH19mTVrFmvWrMHb2/uW/ZX6zULoPqnH/AjFx8fTtWtXLl68SP369R/adqV+sxAPjtRjfsxpNBrs7e0faigj9ZuF0HlS9vMRiomJoU2bNg99u1K/WQjdJkMZQghRh2QoQwghnkASzEIIoWMkmIUQQsdIMAshhI6RYBZCCB0jwSyEEDpGglkIIXSMBLMQQugYCWYhhNAxEsxCCKFjJJiFEELHSDALIYSOkWAWQggdI8EshBA6RoJZCCF0jASzEELoGAlmIYTQMRLMQgihYySYhRBCx9z3zVgLCwsNMzIy6qY1QgjxmMvJybnvddxXMOvr66ddu3btt9OnTxvcd0uEuA/Xr1/3UBSlfv369aMMDAwKH3V7xNOtrKzsl/tZ/r7uki2ErnjjjTciAU9FUTw/++yz6EfdHiHuh4wxCyGEjpFgFkIIHSPBLIQQOkaCWQghdIwEsxBC6BgJZiGE0DESzEIIoWMkmIUQQsdIMAshhI6RYBZCCB0jwSyEEDpGglkIIXSMBLMQQugYCWYhhNAxEsxCCKFjJJiFEELHSDALIYSOkWAWQggdI8EshBA6RoJZCCF0jASzEELoGAlmIYTQMRLMQgihY/QedQNE3VEU5fNH3YZH5dSpU8OLi4sbe3l5/WBubp77qNvzCFzT09N7+1E3QtQNCeYniKIoyqNug3hksvX09KwedSNE3ZChDCGE0DESzEJnnDp1isLCwkfdjMeGHK8nlwSz0AlbtmzBz8+Pa9euPeqmPBbkeD3ZJJgFujA0/Tj1/OR4CSFqTVEUxdzcXOnbt6/SrVs3pX79+oq9vb0yd+5cpaSkRKnk4eGhvPLKK8qiRYsUa2trpWHDhkpeXp5SXFyszJ49W7G1tVXq1auntGvXTtm1a5e63NmzZxUzMzPl559/Vry9vRUjIyPFyclJ2bx5s/Lhhx8q9vb2ipmZmTJkyBDlypUr6nJ3a1NQUJACaP0LCgpS7tUXX3yh5Obm3tMyQ4YMUZycnJSRI0cq5ubmiqWlpTJ69GglMzPzrsdLURRl69atiru7u1KvXj3FwcFBCQwMVMrKyu7reN2tTTUdr82bNxc86vefEKIGlSFob2+vfPfdd8qJEyeU2bNnKwYGBsrf/vY3raCxsLBQXnzxReX3339XDh06pCiKoowfP14xNDRUFixYoHz//ffKkCFDFD09PeXo0aNq0ABKy5YtlZ9//lkJDg5WPD09FUDp0aOHcuLECWX79u2KqampMnr0aK1gvlObMjMzlRkzZiiA8vPPPyvHjh3TCsbaeuGFFxRzc3NlwYIFyh9//FHrYDYxMVGWL1+unDp1Slm/fr1iaWmptG/fXv3guN3x2rJliwIoo0aNUv79738r7777rqKnp6cEBgbe1/G6W5tqOl6XL1/OftTvPyFEDSpDMCAgQCt83njjDcXIyEjJzs5Wg8bKykq5du2aOk9sbKwCKPPmzVOnlZeXK87Ozkrv3r21gua7775T56nsvUVFRanTJk6cqDRv3lx9XJs2rV+/XgGUrKysWgXq7ezfv1/p1auXYmFhobz//vt3DeghQ4Yovr6+WtO++eYbBVB2796tKLc5XuXl5YqdnZ3Ss2dPrWX/8pe/KGZmZkpBQcGfPl61aVMNx+vqo37/ibojY8xPgQEDBlBSUsKZM2fUaX5+fjRs2FB9/OuvvwIwfPhwdZqenh79+vUjNDRUa33169dX/29iYgKAsbGxOq1FixZcvXrnnKipTbV1+fJlUlJSSElJITtbu6M4YMAAQkJCOHDgALt27aJt27b3vP4BAwYAcPLkSXVa9eOVkJBAenq61vEC6N+/PwUFBcTHx6vT6up4VW+TeHJJMD8FGjduDEBBwf8PQ5qammrNk5eXB0DTpk21pltaWlJQUKC17N3o6end9QeymtpUW6+++iqtWrWiVatWvPfee7c8f+LECRYuXEhqairTpk275/Wbm5ujr6//p48XQHp6eq23V5vjVVObxJPL8FE3QDx4Fy9eBMDe3v6287Ro0QKA7OxsbG1t1emZmZnUq1ePBg0aPJQ21eaMh8DAQLWH2apVK3V6cHAwCxcu5Ny5c7z99tt88803mJub33PbMjIyKC8vr/XxqiozMxOqfPDUldu1SRfOEBF1T4L5CacoCkFBQTRu3Bh3d/fbzte5c2f09fXZt28fXl5eABQVFfHzzz/TtWtXDAwMHmibKocJMjIysLa2vuPyPXr0qHH63Llz6d27N7t27VJ7rn/GF198AUC3bt1uO4+NjQ2Ojo7s27dPq1e+c+dOGjZsiI+PDwkJCX+6DXdr070cL/H4kWB+Au3YsQNbW1saNGjA999/z5EjR1i6dKnWGGl1Tk5OjBs3jgULFlBaWoqTkxObNm0iMzOTbdu2PfA2devWDUNDQ6ZPn87EiRO5efMmb7755j1tIzg4+E/17KOiopg9ezYuLi6cOHGCzZs3M3DgwDsGM8D777/PuHHj+Otf/0r//v0JDg5m165d/Otf/7rjsa6LNlU/Xjdu3DC+60qFEA+fUnEGhJ+fn+Lt7a0YGxsrTk5OyrJly7R+4a88L7e6mzdvKm+//bbSrFkzpV69eoqPj4/y888/q89XnmWwZ88eddr27dsVQElISFCnLViwQDEwMFAf16ZNiqIomzdvVpo3b65YWFgo/v7+tzz/IAwZMkSxt7dXevfurZiamipNmzZVJk+erJ6nrNzheCkVZ0e0+T/27jyuirL///jrsAgiCIKogCwKosjivqWpYe5LrmW55O2dZplat2aLy+1dprmkqbnv1Tdvb80lM0ultFzuTIVYDpugiOACuIAicOBcvz9umJ9HwS2Mo32ejwd/nDkz13XNzDnvc801w0y9esra2lr5+PioOXPmKKPRqNQf2F730yZ12/bq3LlzQUV//oQQpVBlXJpW0cyxTSVKuzStoj1km+RyuSeIXJUhhBBmRoJZCCHMjNwo/wmi5NqpvzK5Uf4TRK7KeLI0qegGPEKVgC/KejMzM9O7qKjIxtnZ+Yy1tXVpJ8LygJcfbRMrVGFFN0CUH+kxi8eCUsoOuPEHirip0+nK979khHhEZIxZCCHMjASzEEKYGQlmIYQwMxLMQghhZiSYxRPh/Pnz9O3bl9TU1IpuihBC/DUopezu9v/IX375pXJzc7vbLLlA+d0iTwgh/upKgvngwYOqadOmqnLlyio4OFjFxcWp//u//1NWVlbK2tpaValSRU2YMEEppdS3336r7O3t1Ycffqj8/PyMwIaKXg8hhHhiKKXsjEajcnV1VdOnT1eZmZlq8+bNqrCwUCmlVJs2bdTcuXNNusgff/yxsrCwUF988UVJj/mP3YtTCCHE/6eUsisqKlLOzs5q2LBh6saNG1oAFxQUKFtbW3Xw4EGTYB4yZIjq27fvrUMZQjwW5OSfeGxYWFiwb98+9Ho9vr6+7Nu3D4CIiAgMBgPNmjUzmT86OppOnTpVUGuFEOIJd+vJv6KiIjV06FAVGBiolFJqyZIlqmHDhia9ZYPBoGxsbNSBAwekxyweO3ITI/HY+Oabb3B0dCQgIIArV67g5+cHwKVLl8jIyOD06dMopahbty6JiYnk5+cTFBRU0c0WQognk1LKbvz48crJyUk5OjqqAQMGqPPnzyullIqOjlZeXl7K2tpaDRo0SCml1ObNm2+/fE56zOKxIXeXE48Fubuc+CuRk39CCGFmJJiFEMLMSDALIYSZkasyxGNBp9PlKqUcynp/woQJvyqlGrq7u7d89913Y//c1glRvqTHLB4bOp3uell/eXl5xvz8fJKTk3PLmqei2y/E/ZJgFkIIMyPBLIQQZkaCWQghzIwEsxBCmBkJZiGEMDMSzEIIYWYkmIUQwsxIMAshhJmRYBZCCDMjwSyEEGZGglkIIcyM3ChfPHZGjRpVpNPpLJRS9zN70erVq+VmXeKxIj1m8TjapJTK1+l03O2v2IWKbaoQD06CWTx2LC0tp+p0Oqv76DHn6XS6j/6cVglRfiSYxWNnxYoVZ4xG4yrAcI9Z827evPn5n9QsIcqNBLN4LFlYWHwClNlrVkoV6HS697/44os/8gBXISqEBLN4LK1atSoJ+BLIL2OWbGDNn9wsIcqFBLN4bBUWFk4BTK7OUEqhlCoA5q9atepeQx1CmCUJZvHYWr9+fapSagVQUDKt+GqMGwaDYVGFNk6IP0CCWTzu5gLWxT1llFIG4P0NGzbkVXTDhHhYEszisbZmzZpzSqnPdTpdfnFv+fLVq1dXV3S7hPgjJJjFY8/KymqKUspCKVWk0+lmbdmypaii2yTEH1Eu/5K9cuXKpoBXeZQlxMM4d+7c6IKCgnaenp6vWFtbF9zHIkKUO51OZxw9evQ3f7SccrmHgLW19XuVK1fuaWtrK18IUSFq1KgBYARWVXRbxF+WLiMjo0p55Gp53dzFsn79+pV9fHwql1N5QgjxWDEajWzbtq1chtFkjFkIIcyMBLMQQpgZCWYhhDAzEsxCCGFmJJiFEMLMSDALIYSZkWAWQggzI8EshBBmRoJZCCHMjASzEMWuXLnCt99+W9HNEEKCWQiAtLQ0GjZsyNy5cyu6KUJIMIvyZTAYiIyMrOhmPLD8/Hzy88t6fKAQfy4JZvGHfPXVV7Rt2xZ3d3eaNGlCv3796NGjB7/99hv877l8zJs3j8aNG1OrVi2aNm3KvHnzKCws1Mrw8fFh0KBBTJo0icDAQOrUqcPgwYNJSUkxqSsyMpKBAwfi6emJt7c3gwYNMvkRWL58Oc7OzgwfPpw+ffpQu3Zt/P39yc7OZseOHbRu3Rp3d3e8vb0ZOHAg0dHRUNxbbt68OQDR0dE4Ozvj7OxMWloaANnZ2UyaNIkGDRrg5ubGU089xZdffqnVGxUVhbOzM23btmXMmDH4+vri5ubG4cOHH/HWF0+q8rq7nPgL+uKLL5gwYQI2Nja0bNmS2NhYjhw5QsOGDQkKCkIpxciRI/n2229xdXWlefPmxMXFMXv2bBITE1m16v/foTMsLAwnJyd69epFYmIie/fuJSUlhV9++QUrKyt+++03+vbty82bN2natClFRUWEhYVx9OhRvv/+e4KCgrSyvv32Wxo0aEC/fv2oWbMmVatWpaCgAIPBQIsWLTh37hw//vgj0dHRhIeHY2dnR5cuXdi7dy+Ojo6EhoYCYGdnh8FgYMCAAZw4cYLatWvTunVrjh07xvjx47l+/TpjxozR6o2NjSUjI4OePXty5coVnnrqqT95j4gnhQSzeGhfffUV/O+hqHTr1o2LFy/SqFEjEhMTsbCwYM+ePXz77beEhITw3XffYWdnR05ODp06dWLr1q2MGzeO4OBgrbz9+/dTt25dAEJDQ4mIiOD48eO0bt2aSZMmcfPmTdasWUP//v0B2LhxI2+99RYff/yxSQ/W29ubsLAwKlf+/3ehHTRoEM8//7z2etiwYezevZtDhw7RuXNnZs2axd69e/H09GTt2rXafJs2beLEiROEhISwZ88eKleuzLFjx+jWrRtz5szhb3/7mzavpaUl33zzDQ0aNHhk21z8NUgwi4dmNBoBsLD434iYra0tSikKCwspLCxkz549ANjb2zN79mxtuZLAPHnypEkwe3n9/4fgNG7cmIiICM6cOUPt2rWJiorC2tqa8PBwwsPDAcjLy9PKuVW3bt1MQhng/PnzfPrpp/z000+kp6eXPE2bM2fO3HUdDxw4AMDQoUO1Mlu2bEm9evVITEwkJiYGa2trABo0aCChLMqFBLN4aKNHj+a3337jlVdeoV27duj1egwGA6GhoVSpUoULFy4AcOTIEY4cOXLH8ra2tmWWXfKewWDQyjEYDCxduvSe5djb25u8vnbtGl26dCE9PZ1mzZrRrl07wsPDiYyMJDc3967rmJWVBUDNmjVNpru4uJCYmMiVK1dKnp5yR71CPCwJZvHQ+vfvz4YNGzh8+DA///wzNjY2DBo0iI8++giAqlWrArBgwQJGjBjx0PWUlFOrVi30ev0DL79r1y7S09Pp3bs3GzduBGD+/PlERkailDKZt+QooISLiwsAmZmZJtNLfixK3heiPMlVGeKhbdq0icOHDzN69GjOnTtHUlISK1eupHr16gC0bdsW/vewXpNg+/XXXx+oHj8/P2rWrMmFCxdYs2aNNj0jI4OkpKR7Ln/9+nW4bajk2LFjUHzVCICDgwP876Gu3Lx5E4CCggLatWunrWvJ5XR79+7lzJkzuLi4EBgY+EDrIsT9kB6zeGgll5PFx8czc+ZMUlJSsLe3p0ePHnTu3JnBgwezevVq4uPjadq0KQ0aNCAzM5MzZ85w4MABQkJC7qseCwsLpk2bxhtvvMHkyZNZtWoVVatWJT4+ng4dOpic+CtN69at0el0rF69mtOnT5Oenk5ERAQAp06dAsDV1ZU6depw+vRpWrZsiaOjI6+++iqDBg1ixYoVHD9+nFatWuHp6aldCjhlyhRtfFmI8iQ9ZvHQXnzxRRo1asShQ4dYsGABX3/9NRs3buSFF14gLCwMOzs7vv32W4YPH46dnR3h4eHk5ubSv39/HB0dH6iul156iQ0bNtC0aVNSU1PR6/XUrVuXTp063XPZxo0bs3jxYmrXrk1YWBgWFhZs2bKF+vXrEx4eTkHB/x7uvnr1akJCQsjIyOD8+fO4uLhga2vLjh07eOmll7h+/Tq//fYbvr6+LFu27A8NzwhxN7ryKGTdunXbGjdu3M/Hx6c8ihOPkT179tCuXTscHBwwGo1MmzaN5cuXM3nyZN59992Kbp4Qf5qSp2SPHj36D49EyFCGeGirVq3i3XffxdraGg8PDwoKCkhPT8fCwoKOHTtWdPOEeGzJUIZ4aJ06dWL48OHUrFmTtLQ0CgsL6dGjB7t27aJ169YV3TwhHlvSYxYPzdfXl08//bSimyHEE0d6zEIIYWYkmIUQwsxIMAshhJmRYBZCCDMjwSyEEGZGglmIMuzatQtPT887bmwkxKMmwWwGBg4cyJtvvmky7ejRozg7O5Oenv5I6szMzCQoKIjt27f/oXJu3LhB9erVtccxlfw1bNiw3Np6q/HjxzNo0KBHUvbtIiMjCQ4O1u43TfF2++c//0mrVq1wd3enbt26zJo1609pz4P6/PPP+fjjjyu6GeIhyHXMZiA6Opp+/fqZTDt+/DguLi64u7s/cHk3b96840bxt7/n4OBA165d8fX1fag2l5Sj1+sxGo3s3r3b5BaYd7vX8h8RFRXFM88880jKvt3vv/9O48aNtdfR0dEMGjQIPz8/pk2bho+PD6mpqdpN9/8sRUVFWFpa3nO+xYsXM27cuIeux2g0mvwoiT+PbPUKdvHiRS5dukTLli1Nph8/flx7usfEiRNNeolnz57F2dmZqKgoAL788kvtzmc9e/akQ4cO5ObmkpeXx/Tp0wkKCtIeQDpy5EjS09Nxc3Nj/fr1lNzfZPbs2bz22muMGzcOb29v6tWrx9atW6H4SSGllUNxUHp7e9OmTRv8/f21v5JbbK5evZrmzZvj5uZGYGCgSe9yxYoVNGvWjFq1avH0009r90b+97//Tdu2bXFzc6Nx48bs2LEDim/RGRcXV+pd6YxGIwsWLCAkJITatWvTo0cPEhIS7lnXzz//TOfOnXF3d8fPz4/BgwdjMBiguMfcqFEjKP4hGjJkCO3bt2fnzp306tWLoKAgunfvTrdu3bR6zp8/rz2Q1dPTk6FDh5KdnQ1Av379mDNnDn379sXd3Z2WLVua3F+6rPW+fv06Li4uzJkzh44dO2qflRs3bvD+++9Tv359PDw8aNGiBbt27QKgTZs2JCcnM3XqVLy8vMjIyABg9+7dPP3007i7u9OqVSu+//57rf5OnToxYcIE+vXrh7e3N6mpqff5KRblTYK5gpWEa2hoKJ6entrf7t27tQeM6vV6k4eNljzOqH79+nz33XdMmTKFJUuWkJiYiKWlJQ0aNMDOzo4PP/yQgwcP8sMPP/Drr78SHh5Ox44dcXd3Z9GiRXh5eWk3oc/NzSUsLIwePXoQHR1N+/btWbBgAUCZ5VAcXufOndPaXbduXe0ex5988gmfffYZq1ev5ty5c7Rr10578vWsWbNYuHAhM2fOJD4+ntWrV6PT6Vi6dCnvvPMO7733HgkJCYwYMYIZM2YAkJCQQH5+vsm2KDFlyhR27tzJ1q1biYuLw8nJicmTJ9+1rkOHDjF06FDGjBnD2bNnmTZtGtHR0VhbW5Oens6lS5e0YN6yZQtXr15l3rx5ZfYiL1++TLdu3cjLy+Onn37ixIkThIeHs3v3bih+ksr333/PzJkziYiIwGg0smHDBoC7rndcXBxKKTIyMti/fz8///wzSilefvll4uLiOHDgAElJSRgMBq5evQrARx99hL29PSkpKZw9exZXV1e++eYbxo0bx4cffsjp06d54YUXePXVV8nNzcVoNBIfH09cXBxr165Fr9fj7e39hz/f4uHIUEYFi4qKolGjRiYPAM3OziY0NFTrMcfGxvLKK69o78fExFCvXj0qVarE9u3bGTBggNaLcnd3156Ft337dmbOnImHhwcUP2uvJFRiY2NNxoGTk5N54YUX6N69OwCBgYHEx8ffs5zo6GjeeOMNhg0bBoCVlRVWVlacO3eOuXPnsmnTJpo0aQLA6dOnGTBgABcvXmTRokVs3LhR6206Ojpy7do1Zs+ezdtvv02vXr3Izs4mOjqagIAAbVvZ29trD2wtkZCQwKpVq/j555/x9/cHoGfPnsyYMaPMupRSTJo0iVGjRjFgwAAofv5f06ZNoXgYo0qVKlp5Bw8epE2bNtoPGcXPFoyJiSEgIIC9e/eydOlSbty4wdKlS6lSpQqHDx/m2rVr1K9fX9vGK1eu1H5YvL29sbCwuOd6x8bGUr16dWbPnq1t323btnHs2DF+//13qlWrxo0bN0hLS9Paf/z4cZo0aaLtp6KiIt5//33eeecd7Ud1wIABzJw5k7Nnz1KpUiVyc3P55JNPcHZ2fuDPsShf0mOuYFFRUQQHB1O3bl3tr+T+wMHBwZw7d47s7GyTJ2XExMRoX257e3vt6SAnTpxg586d9OzZU3uv5BB2xYoVZGRk0KVLFyjuhd8azDExMSZ1JCUlaaFUVjlFRUXo9XpatWqltb1kCGPXrl3UrFmT0NBQAPLz84mKiqJp06YcPXoUS0tLrS0lTpw4QW5uLsuXL6dOnToEBARgNBpZsmSJtq0aNmx4R481LCzsjqeJZGVlUb169TLrio6OJiEhgeHDh2vTjh8/rgVbZGQkgYGBWl1ZWVl33EN61apV2roDHDp0CJ1OR/369fH09GT8+PEsXLhQu4f07fvx1KlT+Pv733O9Y2Njeeqpp0xuyr9t2zZ69epFtWrVAAgPD8fW1lZ7GOyJEydo1qyZNr9eryc9PZ0OHTpo00o+Ny4uLsTGxuLs7Fzq0Yj480kwV7Do6Og7nqwcFxeHra0t9erVIy4uDmtra+rVqweAUorjx49rX6CpU6dia2tL69at6d+/v8l49OrVq9m3bx8hISEsWbKEjRs3aoentwZzTk4OqampJkEdHR2tvS6rnMTERPLy8kp9MvTZs2epU6eO9josLAyDwUBISAg5OTnY2tqWOiSg0+mIjIwkPDyc1NRU1q1bpz2qKjo6utTx5ezs7Dselrpr1y46d+5cZl0pKSlYWVnh6ekJxcF77Ngx7WRfTEyMyfaoWbMmkZGRJpfOeXl5kZqaahJmI0eOJDExkbi4OE6cOKHti5iYGKpWrUrt2rW1Nt+6ze+23nq9Xus9Gbq9AAAgAElEQVQ939r+W+9/vmfPHoKCgrSTgidPnjTZViXj3LVq1TLZRk2aNMHV1fWOH2pRsSSYK1Bubi7JycnaoW6JuLg4AgICsLS05ObNmxiNRi5dukR+fj5TpkwhLS1N63m5uLjg5+dHWloaa9eu5dVXX9XKadSoEQaDAScnJzZu3Mizzz4LxSccMzMztS+iXq/HwsJCa0dhYSHx8fHa+2WVExkZiZ2dncmz9Ep4eHiQmppKXl4ely5dYsaMGVhbW2NjY0PTpk25du0aCxYsICMjgwMHDpCamkpwcDA2NjYsXLgQo9FIXFwcycnJWpnR0dG4uLiQkJCg/V24cIGQkBASExM5fvw4eXl5zJ8/n3PnzvHGG2+UWZeHhweFhYUkJydjMBiYNGkSBoNBe9J1VlYWRUVFWt39+vUjPj6eUaNGcejQIcLDw/n0009JSkrS9kXz5s3ZunWr9oN14MABbfnbgz46OhqAgICAe6737cNOJdu35LFYv/32G+vXr9faXlhYyJUrV9Dr9Zw/f55r167h7++PjY0NmzdvxmAwsHfvXtavX8/06dPLrENUHAnmChQTE4PRaCy1x1zSCwsNDaVFixa0atXK5DFKtx4Sd+zYka5duzJhwgRatGhBTk6O9t7gwYNxd3enf//+DBkyBIqDuFKlSvj5+Wnt8PPzw8bGBorHbAsKCky+qKWVExUVRb169Urt+Q4fPhxfX1+aNWvGc889R/v27cnPzyclJYXAwEDmzZvHhg0bCAkJYdq0aVhZWeHq6sqyZcvYsmULgYGBjBw5UhvWSUtL4/Lly8yZM4fWrVtrf6tXr6Z79+6MGTOGl156iYCAACIiIti9ezc1atQos64mTZowduxYevbsydNPP42dnR2urq7auPro0aMJCwvj66+/huLx5CVLlhAXF8fgwYN58cUX2b9/P8uWLaN9+/YAvP322wQHB9OnTx9atGjBvn37TPb17cHs4eGBo6PjXdc7MzOTjIyMO3rM06ZNQ6/X07hxYyZPnkzXrl21q1CsrKwYNWoUixcvpk2bNiQlJeHq6srSpUtZuXIlderUYf78+WzYsEEb2oiNjb2jDlFx5NFST5D09HSCgoI4cuTIHWG/ZcsWpkyZYnIJ2cMor3KEeNLIo6UEFPesd+zYQd++fbG2tmbFihXUr1+fevXqsWPHDrKzs+nQoQNXrlzh888/104KPojyKkcIcf9kKOMxlp+fr/2DRO/evSksLGTnzp1YWlpSVFTE8uXLadOmDaNGjSI0NJS5c+c+cB3lVY4Q4v7JUIYQQpSD8hzKkB6zEEKYGQlmIYQwMxLMQghhZiSYhRDCzEgwCyGEmZFgFkIIMyPBLIQQZkaCWQghzIwEsxBCmBkJZiGEMDMSzEIIYWYkmIUQwsxIMAshhJmRYBZCCDMjwSyEEGZGglkIIcyMBLMQQpgZCWYhhDAzEsxCCGFmJJiFEMLMSDALIYSZkWAWQggzI8EshBBmRoJZCCHMjASzeKK0a9eOxYsXV3QzhPhDJJjFHXbs2EHnzp3x8PCgYcOGvPbaa1y6dOm+l588eTK7du0ymTZnzhw6duz4CFr7/+Xl5ZGQkECjRo3KnKdPnz68//77j7QdABcuXGDo0KGkpaU98rrEk0eCWZhYuHAhY8aMoVu3buzZs4f58+fz66+/8sorr9zX8unp6axZs4aAgACT6Y0aNaJLly6PqNX/ExMTQ2Fh4V2DOTQ0lObNmz/SdgD88ssvnDx5Eg8Pj0del3jySDALzcmTJ/noo4+YN28eEydOJCQkhB49ejB9+nQOHTrE5cuX2bx5M506dWLixInUq1ePgIAA5syZA0BaWhrNmjXDwsKCZ555hmeeeQaA8ePH89JLL3Hz5k2trt27d/P000/j7u5Oq1at+P777wHIzMwkKCiIjRs30qZNG9zd3enTpw83btwA4MaNG7z//vvUr18fDw8PWrRoofXOf//9d3x8fHBycip1/Zo1a8YHH3xAlSpVAJg9ezavvfYa48aNw9vbm3r16rF161YAEhISCAgIYN68eTRp0gQfHx9GjhzJ9evXAWjdujWLFi3Syv7qq6+oU6cOAFu3bmXs2LFkZWXh6emp9dDXr19Po0aNqF27NgMGDKCwsBCA119/nRYtWpCXl1fOe1Q8riSYhWb58uX4+fkxZMgQk+nu7u4AZGRkkJ2dzenTp+nSpQsnTpzgnXfeYc6cOfz66694eHjw+uuv06lTJ1JTU/npp58A+PTTT7VhEYBvvvmGcePG8eGHH3L69GleeOEFXn31VXJzc7G1tSU9PZ2jR4+ybds29uzZw+HDh9m/fz9KKV5++WXi4uI4cOAASUlJGAwGrl69CsXBfLfe8n/+8x8ArR25ubmEhYXRo0cPoqOjad++PQsWLACgqKiIixcvYmdnx4EDB9i8eTNhYWGsXLmS/Px8kpKSCAoK0sqOiYkhMDAQgIEDB9KkSROmTp1Kamoqs2bNIjY2lokTJ/LJJ58QHh7O3//+d6ysrABwcHDA0dERS0vLctyb4nEmwSw0hw4domfPnlhYmH4szp49C0DNmjVJS0vjmWeeoWvXrlStWpURI0bg4OBAQkICAMePH6dZs2Ymy+fk5JCWlkbDhg0pKiri/fff55133qFjx47Y2NgwYMAAcnJyOHv2LMnJyeh0OubNm4ebmxshISFYW1tjYWHB9u3bOXbsGGvXrsXNzY2ioiLS0tJo2rQpFAdz48aNy1y/uLg4HBwc8PT0BCA5OZkXXniB7t274+DgQGBgoLbuaWlp1KhRg7Fjx+Lo6EirVq146qmnSEhIID4+nqKiIi2IAfR6vRbUBoOBqKgok+1Q0js+c+YM1atXp0ePHtp7c+bMYf/+/VhbWz/0vhNPFglmocnIyKB27dp3TN+7dy+BgYE4OTkRGxtrEkgFBQXcuHEDZ2dnjEYjERERdwSzXq/H0tKS+vXro9frSU9Pp0OHDtr7mZmZALi4uBATE4OXlxcODg4ApKamUlBQgL+/P9u2baNXr15Uq1YNgPDwcGxtbWnQoAEFBQXExcUREhJS5vrp9XqTse9be7kASUlJ+Pv7A9yxngBXrlzBxcUFvV5P9erVqVWrlknZJfNHRUVhMBhMeu/BwcGsWbOGBQsWEBoaSmpq6l33hfhrk2AWmlq1anHq1CmTaZGRkWzfvp0xY8ZAcQCVDAUA7NmzB1tbW9q2bUt8fDw5OTl3DCfExMRQt25dbG1tyc7O1uoqsWvXLpo0aYKrqysxMTEm5UdHR2NjY4Ovry8pKSn4+PiY1B0UFISlpSWJiYkUFBSYLHu7W9uek5NDamrqHXWVvL49xNPS0oiMjKRTp07ExcWZvJecnExGRobWYz5x4gR+fn7aWHaJ/v378+uvv3Lz5k2WLFlylz0h/uokmIVm0KBBfP7552zatImoqCjWrVtH//79ee655xgyZAjZ2dmkpaWRm5vLlStX2LVrF2+//TbvvfceTk5OZGRkQPGQQlJSEkopuC0Q/f39sbGxYfPmzRgMBvbu3cv69euZPn26Nu+tPdXo6Gj8/f2xsrLCw8ND++H47bffWL9+Pfb29gBkZWVB8dhwWW5th16vx8LCgvr160PxUEN8fLzJ+4WFhVy7do3jx48zbNgwOnToQKdOncjLy+Py5csYDAYuXbrEhAkTsLS0pEGDBlB8BJCVlUVKSgpnzpwBYN26dSQkJHDp0iVyc3O1E4UnT56kadOm7N+/v1z3pXi8WVV0A4T5eOeddygoKOCDDz4gOzsbf39/3n//fUaMGAHFYWVvb8/ixYsZO3YsXl5eTJkyhZdffhmAFi1a0KpVK1588UWqV69OTEyMtlxoaCgArq6uLF26lJkzZ/Lhhx/SsGFDNmzYoA1txMTEMHz4cK1Nt/agp02bxquvvkrjxo2pVq0aXbt25eTJkwA0b96czp078/zzz3Po0KE71i0vL4/k5GStrJiYGPz8/LCxsYHiqzBKetxFRUUkJCRga2tLYGAgVatWZcCAAUyZMgWAoUOH8sMPP9CgQQPq16+Pm5sbvr6+VK5cGYB+/fqxadMmWrZsSY8ePVi6dCk7duxg+vTpVK1alUGDBjF69GgA8vPzycrK0q46EQJAVx6FrFu3blvjxo373XqYKZ4869evZ9OmTezdu7eim/JIJSQk0KZNG86dO6eFrRD3YjQa2bZtW9Ho0aP/cIdXhjLEfdPr9drJsSeZXq/H09NTQllUGAlmcd/0ej316tWr6GY8crGxsX+J9RTmS8aYxX3bvXt3RTfhT/Hee+9VdBPEX5z0mIUQwsxIMAshhJmRYBZCCDMjwSyEEGZGglkIIcyMBLMQQpgZCWYhhDAzEsxCCGFmJJiFEMLMSDALIYSZkWAWQggzI8EshBBmRoJZCCHMjASzEEKYGQlmIYQwMxLMQghhZiSYxX2bO3cugwYNquhmPBSj0ag9Yftx9+677/L8889rrxcsWICzszPOzs5MnDjxT2lDRdRZ4pVXXuHatWvlUtaIESO09Vi3bl2Z006fPl0u9d0vCebHzIULFxg6dChpaWnatMzMTIKCgti+ffsjrTs+Pv6xfORSYWEhI0eO5MqVK9q0yMhIBg8ejI+PD3Xq1KFv3778/vvv913mf//7X/72t7+ZTAsLCyMoKOiRf4l///13GjdurL2OjY2lY8eOxMbG8sEHHwAwZswYateuTZ06dejYsSPLly8nLy9PW2bOnDl07NjxvuqbPHkyu3btMplWWp0PsvzDOn78ONu3byc6Orpcylu4cCE//PADAA0aNChz2meffcavv/5aLnXeDwnmu7h58+afWl9RUdE95/nll184efIkHh4e2jQHBwe6du2Kr6/vI21ffHw8fn5+AFy9etXki17eytoWD7NP5s6di6+vLy1atIDibditWzecnZ3ZsmULX331FZaWlgwcOJDLly/fV5mbNm3Cysr0yWze3t506dIFZ2fnB27j/TIajcTExNCoUSNtWmxsLMHBwdSsWZMqVaoA8PPPPzNmzBi2bdvGoEGD+Pjjjxk6dKi2TKNGjejSpcs960tPT2fNmjUEBASYTC+tzgdZ/lb387kvMW3aNJRSREVF3fcyd1OtWjWysrIAtDaWNi00NJSFCxeWS53344kL5tjY2IdaLi8vj+nTpxMUFIS3tzcDBw5k5MiR2vv//ve/adu2LW5ubjRu3JgdO3YAsG/fPjp06MBnn31GYGAgnp6evP/++9py58+fZ8yYMfj6+uLp6cnQoUPJzs4GYPr06fTu3ZvXXnuNgIAA/v3vf0Pxl6pz5864u7vj5+fH4MGDMRgMbN26lbFjx5KVlaXVk56ejpubG+vXr8fHxweKv7wLFiwgJCSE2rVr06NHDxISEgCYPXs2r732GuPGjcPb25t69eqxdevWe26foqIikpKS8PPzIy8vj6FDh7J48WLi4+NxdnYmIyMDgOvXr5sMd5w+fRo3Nzet/tWrV9O8eXPc3NwIDAxk1qxZAOzduxcvLy/mz59PixYtGD9+PABffvklrVq1wtPTk549e9KhQwdyc3NJS0vjhRdewMvLi+DgYHbu3Flqu1NSUlixYgVjxozR2vfqq6/Sp08fli1bRosWLWjTpg0rVqwgKyuLQ4cOkZCQQEBAAPPmzaNJkyb4+PgwcuRIrl+/DsDEiRP58ssv2b17N56ennzzzTf85z//oWXLlkRERODo6AhAXFwcAwYMoHbt2gQEBDB//nytXf369WPOnDn07dsXd3d3WrZsiV6v194vazslJiZy/fp1rcdcWFjIqVOntJ4dQEZGBhcuXKBr1640adKEsWPHsmjRIn788UfCw8MZP348L730ksmPXHJyMkOGDMHLywtvb282b95MWloazZo1w8LCgmeeeYZnnnmmzDqPHDnCM888g4eHB+3atSMxMbHM5cva1/eyc+dOYmJi6NKli0mPueSIcePGjbRp0wZ3d3f69OnDjRs3mDBhAgMGDDApp3fv3rzyyiva67i4OGrVqkW1atXKnFarVi1++eUXDAbDfbX1j3pigjk6OhofHx/atm1Ly5YtH3j5Dz/8kIMHD/LDDz/w66+/Eh4erh3qLV26lHfeeYf33nuPhIQERowYwYwZM6A4BPV6PUopjh49ytSpU7Uv+eXLl+nWrRt5eXn89NNPnDhxgvDwcO2hprGxscTFxTF27FhiYmIYMGAAhw4dYujQoYwZM4azZ88ybdo0oqOjsba2ZuDAgTRp0oSpU6eSmprKrFmzcHd3Z9GiRXh5eVG1alUApkyZws6dO9m6dStxcXE4OTkxefJkAHJzcwkLC6NHjx5ER0fTvn17FixYcM/tk5ycTH5+Pl5eXgwbNgxbW1vefPNN6tWrR+XKlbUfxPnz5xMWFqYNtcyfP58BAwbg7+/PJ598wmeffcbq1as5d+4c7dq1IyUlRdsWubm5eHl58dtvvzF37ly+++47pkyZwpIlS0hMTMTS0pIGDRpgZ2fHlClTADh27BgrVqygadOmpbZ7+fLlhIaG4urqCsDXX39NVlYWU6dONZnP1dWVSpUqkZmZSVFRERcvXsTOzo4DBw6wefNmwsLCWLlypfZZsbS05NtvvyU1NZU+ffowaNAgnn/+eRo2bAjAmTNn6NGjB88++yyJiYmsWrWKjz/+mKNHjwJw7do1vv/+e2bOnElERARGo5ENGzYA3HU7RUZGUr16de2IKSkpiYKCApOQjIyMxMLCQmsLoH2WU1JS+PTTT/Hw8NDev3DhAj169MDJyYmDBw9y4sQJnn76aTw8PHj99dfp1KkTqamp/PTTT6XWqZRixIgRdO3alaioKCZNmkTdunXLXL60fX0vBQUFfPDBB7z++uu0bt3apMdsa2tLeno6R48eZdu2bezZs4fDhw+zf/9+AgICSExM1OaNjo7m8OHDvPrqq9q02NhYk+1X2jSj0cjNmze5dOnSPdtaHp6YYP7666+1nuipU6cICwt7oOW3b9/OhAkT8PDwoFatWlSuXBkLCwuuXbvG7Nmz+cc//kGvXr1QShEdHa0d4iQlJREcHMy4ceOoWrUqTZo0AcDCwoKlS5dy48YNli5dipeXF4mJiVy7do369etD8c6fOHEiQUFBWFhYYGNjw6RJkxg1ahQDBgzAysqKM2fOaKFjMBiIioqiWbNmJm2PjY3VvmQJCQmsWrWKZcuW4e/vj729PT179iQmJgaKA/aFF16ge/fuODg4EBgYiIXFvT8GcXFxVKpUiX/84x8opfjyyy+pVKkSFhYWBAQEoNfrOXXqFKtXr8bFxYWwsDBOnz7Nzp07mTx5MufOnWPu3LksXLiQJk2aYGlpyenTp7V10+v1dO/eXTupVaVKFbZv386AAQNo2bIltra2uLu7a20tKiriwoULFBUV0bZtWzw9Pe9os9FoZNu2bbRt21ab9ssvv9C4cWNq165tMu/58+cpKCigZs2apKWlUaNGDcaOHYujoyOtWrXiqaee0nr9ERERWFpaEhQUpC2v0+nQ6/Xafpg5cybt2rXjtddeo3Llyjz99NO4u7ub7Id3332XoKAgatSogbe3NxYWFvfcThEREXcMY+h0Ou0zRXH4+Pr6mgwxlIyvV61alZycHNLS0rS2fvrpp7i7u/PZZ59Rp04dqlevjru7OxSP6Zb2ebu1TqUURUVFpKSkYGtrS9++fbG0tCxz+dL29b2sWbOGnJwcxo4di6+vL/Hx8VrvNTk5GZ1Ox7x583BzcyMkJARra2ssLCxo0KABaWlp5ObmArBy5UqaNWumDWtR/Nm+fajl9mklR4T5+fn3bGt5eGKC+daTUjqdjjp16jzQ8vb29trGX7FiBRkZGXTp0oUTJ06Qm5vL8uXLqVOnDgEBARiNRpYsWQLFH7LAwECtnFOnTuHq6kq1atU4dOiQ9gH29PRk/PjxLFy4kKZNm3Lt2jXS09Pp0KGDtmx0dDQJCQkMHz5cm3b8+HHtSxkVFYXBYDD5Ypa0oeRLFhYWhouLi0mbsrKyqF69OgAxMTEm7yUlJeHv73/P7RMfH09BQQEHDhzQeswlgoKC0Ov1vPvuu4wYMYKBAweyf/9+PvnkE1588UW8vLzYtWsXNWvWJDQ0FIo/4FFRUdq6xcbG0r59+zv2SWZmJgAnTpxg586d9OzZE4pP0NStW5cWLVpo++J2er2ezMxM7ceS4sPe20OZ4iEpKysrWrZsSWxsrMk2ojjYXFxctLYEBwdTqVIl7f3CwkISExO1L/P+/ftN9q1SisuXL1O9enVSU1PJzs6+43Pj7+9/z+0UFRVlsv/j4+Px9vbGzs5OmxYVFXVH+/fu3YulpSVNmjRBr9djaWmpBevBgwfp3bs3Op3OZBmj0UhERMQdwXp7nRYWFmzbto34+HiaNWum9YzLWr60fX03V65cYf78+UyaNAkHBwf8/PwoKCggPj4eij/TXl5eODg4AJCamkpBQQH+/v40aNAApRTJyclkZmby9ddfa8NaJW1MTEy8o3d8+7QzZ85gYWFBzZo177vdf8QTE8x9+/ZlxowZ9OnTh9WrV1O3bt0HWn716tXs27ePkJAQlixZwsaNG/H29obioI+MjCQ8PJzU1FTWrVtnEnS3/rLGxMSYHEKOHDmSxMRE4uLiOHHihDb+qtfrsba21k6mUXyYaWVlpfX+srKyOHbsmDaeeOLECfz8/O7oYdwazNnZ2Xd8eHbt2kXnzp3JyckhNTXVpH3R0dEmr8sSHx9Pnz59mDx5Mu+88452coTiYN6xYwfx8fG89957PPvss/z444/s2rWLf/zjHwCcPXvW5McyLCwMg8FASEiIFmq3t2Pq1KnY2trSunVr+vfvz8SJE7XtV716ddavX8+cOXP45z//adKeEklJSQB4eXlp02rWrKlNL5Gdnc2CBQsYOHAgrq6u6PV6k32alpZGZGQknTp10vbD7T+Op06dIj8/n4YNG2I0Grlx44bJfggLC6OoqIj27dsTExND1apVtR+I7Oxsbb/cbTuVtr9KOwyPiooiODhYe33mzBnmzZvHSy+9RLVq1YiJiaFu3braj2tOTg6VK1e+Y/vFx8eTk5Nzx7qWVmejRo20H6OSYabSli9rX9/N/PnzycnJ4aOPPsLHx4fu3btr60kp37no6GhsbGzw9fWlVq1aODk5cerUKTZs2EC1atXo06ePNu/p06fJy8szWZ/Sph07dozAwMD76t2XhycmmO3s7Bg/fjwbNmygf//+D7x8o0aNMBgMODk5sXHjRp599lkAgoODsbGxYeHChRiNRuLi4khOTobiw+n4+HiT3smtX5zmzZuzdetWEhMTycvL48CBA9p8sbGx1KtXD2tra22ah4cHhYWFJCcnYzAYmDRpEgaDAXt7eyju7WVlZZGSksKZM2cAuHjxIpmZmVqdISEhJCYmcvz4cfLy8pg/fz7nzp3jjTfeQK/XY2FhofWUCgsLiY+Pv68vSVxcHG3atGHSpEl4enry1ltvae8FBwdz/fp15syZg729Pe3atUOn0zFs2DDc3Ny0dUtNTSUvL49Lly4xY8YMrK2tsbGxISkpifz8/DsOJ11cXPDz8yMtLY21a9dq44Lx8fH85z//ISsri/Pnz+Pi4qKdcLtVydn+W997/vnniYyM5KOPPiIyMpJdu3bRs2dPKleuzMyZM6H4h66wsJBr165x/Phxhg0bRocOHbRgzsjI4PTp01y4cIH09HRtGRcXF2rWrImFhQWBgYFs376dmzdvEhcXx3vvvcdbb72Fs7NzqUFC8RUAd9tOBoOB7Oxsk6sYbg/J3NxckpKSqFKlCuHh4SxbtozOnTvTsGFDZs+erbX11vqbNWvGunXriI2NJSUlhT179mjrSfHleUlJSSilSq2zZEw3KyuLq1evap2i0pYvbV/PmDGDVq1alXpiLTk5mbVr17JmzRrtc3/mzBk8PT217Xb7UWt0dDT+/v7aVTONGjXim2++Yf369YwcOdLkO1cyLHPr+tw+TSnFf//734fKlYf1xARzeRg8eDDu7u7079+fIUOGQPFJoWXLlrFlyxYCAwMZOXIkBQUFUNxLysvLM/mQ39qDfvvttwkODqZPnz60aNGCffv2afPd3isDtDPoPXv25Omnn8bOzg5XV1ftkK1fv37Y2trSsmVL/vWvf2nlVKpUSet5d+/enTFjxvDSSy8REBBAREQEu3fvpkaNGsTExODn54eNjQ0Uj0cXFBTcM5hLrsgICAjAysqKFStW8NNPP7Fp0yYAGjZsSK9evejRowcANjY2dO/enTfffFMrY/jw4fj6+tKsWTOee+452rdvT35+PikpKcTGxlKrVq1SLzPr2LEjXbt2ZcKECbRo0YKcnBwiIiL44IMPaNSoET/++GOpl64BuLm5YWlpqa0vQKdOnfj000/ZsWMHXbt2ZcaMGXTq1IkffvgBZ2dnioqKSEhIIDw8nMDAQIYPH07btm1Zv369VsbIkSP57bffaNasmXZ97u1ht2TJElJSUvDz82PYsGGMGjVKOwFbWjB7eHjg6Oh41+1kbW3NhAkTmDx5Munp6eTn55OcnHzHEZvRaOTdd9+lb9++7Nq1i6lTp7J9+3Zt6OH2z96sWbPw8vKiS5cuPPvss8TFxQHQokULWrVqxYsvvkjv3r2heGjl9jp//vlnhg4dSosWLbC1teWTTz4pc/nS9nXjxo1JTk4u9bK5f/3rXzRq1IjnnnvOZLqvr2+ZPebbX48YMYLdu3dz5coVRowYYVJOXFwctWvX1oZBSpv266+/kp+fz8svv3xH+x4V3X3Mc0/r1q3b1rhx434ll2s97rZs2cKUKVO0kz2i4qWnpxMUFMSRI0fuOIwuS0FBAfXq1eP333/HycnpvpZJSEigTZs2nDt3rtTDe3NSclXNwYMHTYYuHrc6d+/ezQ8//MDixYvLpbzShIaGEhQUdEcdr3gPUvcAACAASURBVLzyCjk5OWzevLnMaX/72994+umnTS6fLU3xyeai0aNH39lLeEB/uIAnwY4dO8jOzqZDhw5cuXKFzz//XDvJ9Ffx97//vcxrwBctWmRyFvvPEBcXx44dO+jbty/W1tasWLGC+vXrP9B/HlaqVIlRo0Zx9OhRbVzyXvR6PZ6enmYfytxyyO3o6Eh2drZ2ueTjVOepU6f45ZdfmDdvXrm1sYTRaCQsLIxt27YRGxvL2rVrtfeys7O5efMmer1e+0eb0qZ999132Nvb3zOUy5sMZRQfqi9fvpw2bdowatQoQkND7+vayifJ2rVrOXLkSKl/f3YoU3zIXPKPNr1796awsJCdO3dql2Hdr7fffrvU8eeylIz9Pw7i4uJQStG4cWPmzJnzWNbp5+fHxx9/bDLcVF5u3rzJyJEjOXXqFJs2bTI5qTp69GgCAgJMLosrbdq1a9dYtGhRubftXmQoQwghykF5DmVIj1kIIcyMBLMQQpgZCWYhhDAzEsxCCGFmJJiFEMLMSDALIYSZkWAWQggzI8EshBBmRoJZCCHMjASzEEKYGQlmIYQwMxLMQghhZiSYhRDCzEgwCyGEmZFgFkIIMyPBLIQQZkaCWQghzIwEsxBCmBkJZiGEMDPl8pRspZQxISEhLy0traA8yhPiQSmlLAB0Op2xotsi/pqUUjrArjzKKpdgLiwsnJednb01Ozu7PIoT4oGdOXNmrtFo9HR1dX3bwcHhXEW3R/w1KaWKyqOccnlKthAVbfTo0VFAkFIqaPXq1TEV3R4h/ggZYxZCCDMjwSyEEGZGglkIIcyMBLMQQpgZCWYhhDAzEsxCCGFmJJiFEMLMSDALIYSZkWAWQggzI8EshBBmRoJZCCHMjASzEEKYGQlmIYQwMxLMQghhZiSYhRDCzEgwCyGEmZFgFkIIMyPBLIQQZkaCWQghzIwEsxBCmBkJZiGEMDMSzEIIYWasKroB4tFRStlVdBv+LOPHj7dQSlGjRg3bv9B6G3U6XV5FN0KUP11FN0A8GkopK8BQ0e0Qj9RhnU7XrqIbIcqfDGUIIYSZkWAWQggzI8EsHlpmZiY6nY4VK1aYTP/b3/5GixYtKqxd5iQoKIjBgwebTFu3bh01atTg7NmzFdYuYd4kmEW5c3BwoGrVqhXdDLNVuXJlHB0dsbS0rOimCDMlV2X8xSil0Oke7TnfxYsXP9LyH7VHvY1efPFFXnzxxUdWvnj8SY/5CVdyKD1z5kxq1KiBg4MD2dnZACxfvpx69epRuXJlAgIC+PDDD8nL+9/VVxEREeh0Ol5++WXq16+Pra0twcHBbNq06a71+fj4oNPpaNfO9GKBu9WVm5vLiBEjcHFxwcXFhb59+5KSkvLA67pu3TquXr36QMu88cYb1KpVi127duHv74+FhQU//vgjAAcOHKB169ZUrlwZb29vRo4cyfnz57VlnZyc6NKlC23btsXOzg4vLy+mTZtGYWFhmfWNGDECnU6HTqczme9edc2ZMwdPT0/s7e1p164dYWFhD7h1hBAVTillpZRSgYGBysnJSfXu3VsdPXpU7du3Tyml1IwZM5SDg4OaMmWK+uqrr9T06dOVg4ODGjZsmFJKqfDwcAWo9u3bq59++knt3btX9evXTwFqy5YtSimlMjIyFKCWL1+uSvzwww+qSZMmqm3bttq0e9U1depUpdPp1AcffKDWrFmj2rVrpzIzM9WD6tWrl3J0dFT//Oc/1dWrV+9rmbFjxypbW1sVHBys9u3bp7Zv366MRqPav3+/sra2ViNHjlRffvmlWrRokapbt65q0KCBunHjhlJKKUdHR+Xp6an+85//qMOHD6v33ntPWVpaqjfeeEMrPzAwUL3wwgva6xMnTqhhw4YpQBkMBqWUumdd+/fvV4AaMmSI+uKLL9RLL72kduzYoZRShyr6cyaEeAC3BnP16tXV9evXtXBIS0tT1tbWauvWrSYhtWLFCgWoy5cva8G8a9cu7f2ioiIVEBCgmjZtqlQZwayUUl27dtWC+X7qGjp0qLK3t1cFBQX3FaZ3s2fPHtWhQwfl5OSk/vWvf90zoMeOHasA9d///tdkesOGDU0CViml4uLiFKC2bdumVHEwT5w40WSe0aNHK2tra5WVlaVUKcGslFKzZ882CeZ71bVmzRoFqCNHjtzefAnmJ5QMZfwFtGrViipVqmiv9+3bh8FgYMiQIdja2mp/48aNA+DcuXOllmNhYUHnzp2JiIigoKDgvuq+n7qGDBlCbm4u3bt3Jyoq6p5lXrhwgTNnznDmzBmysrJM3uvWrRsHDhzg+++/Z/v27TRs2PCe5VWpUoVWrVppr1NSUtDr9axcudKkzY0aNdLaXJZu3bphMBg4efLkPeu937p69uyJs7MzQ4cOZffu3fdVrni8ycm/vwB7e3uT1xcuXADg22+/pXbt2nfM7+fnR3R0dKllVatWDaPRyI0bN+6r7vupKzg4mN27dzNp0iQaNWrE3//+d5YtW4a1tXWpZQ4ePJiDBw8C8Oqrr95xud7hw4eZNWsWZ8+e5e23375nG8vaPjNmzKB///53zO/u7l5mWdWqVQMgJyfnnvXeb11Vq1blyJEjvPXWW/Tq1Yu2bduyefNmPDw87qsO8fiRYP4LKgkPgAYNGjzQsufOnaNKlSpUq1aNzMzMcqurW7dudOnShUWLFvGPf/wDHx8fpkyZUuq8M2fO1OquU6eONj0sLIwPPviAiIgI3nzzTb766iscHR0faP1ubXNubu5DbR8AT0/Pcq2rfv36fPfdd/z444/069ePESNGsG/fvgdqm3h8yFDGX1BoaCgWFhYsWbLEZPq9esFXr15l27ZtPPXUUwDY2NgAcPny5T9UV35+PhQPlbz11lt4eHjcdSigXbt29O3bl759+2qH/ABTp06lbdu2nD59mn/9618PFcoA9erVw9vbm3Xr1pm0s7Cw8K5DOEop1q9fT7Vq1QgICIDibXS37XO/dZVso9DQUHr37n3fQyVCCDNy68m/208+KaXUm2++qQDVu3dvtXbtWjVz5kxVq1YtdfLkSaVuuSqjTZs2auXKlWr+/PnK19dXWVpaqqNHj2rl+Pr6KmdnZ7Vy5Upt2q0n/+6nrrlz56q2bduqFStWqOnTpytAzZo1q8wTdmUpuVriQYwdO1bVrFnzjunbtm1TOp1OBQUFqaVLl6pFixapZs2aqYULF2rzODo6qtq1a6tPPvlELV++XIWGhipAzZ07V5vn73//u9LpdOqtt95S+fn5SpVy8u9edR07dkx5e3uruXPnqmXLlilnZ2fVpUsXJSf/hHjM3CuYjUajmj9/vvLx8VHW1tbKy8tLvf766+rixYtK3RLM3bt3Vz4+PsrGxkY1b95cfffddyblHDt2TLVu3Vo99dRT2rTbg/ledX399deqefPmys7OTrm5uakJEyaUyxUa96OsYFZKqd27d6tmzZqpSpUqqerVq6v+/ftrPyaqOJhbtWqlQkJClI2NjfL19VXz5s0zKePixYuqf//+ysnJSbtC5PZgvlddv//+u+rWrZuqVq2acnJyUs8995xKTU1VEsxCPGZKgvlhlXa5nDBV2uVyfzIJ5ieUjDELIYSZkWAWQggzI08weUIppSyBmIpux20qPaqCr1696lFUVFTJyckpzdLS8v7+++XBKDN8IswJnU43pKIbIcqfBLP4UyilfIDTFd2OPyBFp9P5VHQjxF+DDGUIIYSZkWAWQggzI8EshBBmRoJZCCHMjASzeGJ06dJFezrI5s2by6XMgQMHlpTpDbxWLoUKIYQ5UEr5lPy7Wt26ddXHH398x7+xXbp0SdWuXVv9+9//vuu/u6Wnp6vnnntOnT171mS6m5ubmj59ujp//vxd/6W7rOVLk5WVpY4ePaqKL5drX9HbUfw1yG0/xZ8qJyeH06dP07hx4zveq1q1Kr169cLf3/+uZfz4448cO3bM5NaaV65c4fz587Rq1YpatWo98PK3KyoqwtLSEmdnZzIyMkoml36TaiGEeByV9Jh//vlnBWg3MCpx7ty5kl6punbtmlJKqYMHD6qmTZuqypUrq+DgYBUXF6f+7//+T1lZWSlra2tVpUoVNWHCBHVruWfOnNHKXL58ufL29lZ2dnaqc+fOymAwlLn8pEmTVIcOHdSwYcNUrVq11Lp167RyZs+erWrUqFH2E1aFEOJxVBLMixcvVm5ubqUOG6xevVr5+PxvxMNoNCpXV1c1ffp0lZmZqTZv3qwKCwuVUkq1adPG5NaaSim1bNkyZW9vr4xGo1JKqaioKAWo7777Tl28eLHk4aVlLt+tWzdVvXp1FRERoYqKitTNmze194YMGaLat29/s6K3ofjrkJN/4k8VERFBkyZNSn0vOjqa4OBgKL7pfFFREadPn6Zy5co8//zzWFpaYjAYCA8PN3lGX8mygYGB6HT/+2fWwsL/dXCTk5NxdXXluf/X3r1HVVXmfQD/7gNHQBSC0AC5GRgX8Yr3lZo6eclxzFuXyZlcy8us1Gqyct4UppohmyYtR8sbshKrqdEm1Jo0tbDMqSzFl5ugAqIiCMhVQTic83v/iHNeToKpnMs+h+9nLVZ19t7P+e3D9uvTs5+zn+nTAaDd4zMzMxEfH48BAwZAo9HA3d3dtC07OxsxMTHW+Jo3UZsYzGRTJ06caHN8GS3haAxmjUaDAwcOICcnB+Hh4aZllE6cOAGdToe4uDizY7Ozs9G3b1/Tfw8cOBAffPABXnnlFQwZMgRFRUXtHl9dXY3i4mJMmDDhupoMBgNyc3MRGxurtudkEBF1jIiE6XQ6cXNzk507d7Y5lNGjRw/54IMPzF7T6/Uyd+5c6du3r4iIrF+/XmJiYq471s/PT9asWXPd67W1tRIVFSVLlixp9/ivv/5atFptmzM5Tp06JQDkq6++KrH3Z0idB2dlkM2cPHkSjY2N8PT0RG5urun1gIAANDQ0oLy83NRj3rNnD7y9vREdHY2qqipEREQAAMrKylBeXo7CwkKICO6++26UlpaioqLCrMe8ceNGjBs3Di4uLqivr7/h8VlZWYiKimpzVe6srCwoioIBAwZwKIOInIuIhL377rummRetfz7//HPZv3+/dOnSxbTc0lNPPSV33HGHeHt7y6xZs6SkpERERLKysiQkJES0Wq3MmTNHREQOHjwoAOTChQsiLWv/3Xfffaalqp5//nnTjcO2jn/iiSfk0UcfbbMX/9e//lVCQ0NFRM7a+zMkIrKo1l8wsbS1a9eKt7e3Vdp+5JFH5IEHHhAGM9kSb/6Rw8vKykJERARKS0vR0GCZWW21tbUoLS1FZmYmYmNjLdImEZGqWLPHPHLkSNOwSOv5yh0xdepUU5vbt28X9pjJlriCCdmEiPgBeMNa7aenp09rbGy8IyYm5hMvL69qK7xFhaIoy6zQLtF1GMzkFBYtWpQJIFZEYpOSktS21iHRLeEYMxGRyjCYiYhUhsFMRKQyDGYiIpVhMBMRqQyDmYhIZRjMREQqw2AmIlIZBjMRkcowmImIVIbBTESkMnxWBjmchQsXGpSWVVdFxGyboiitX8tXFCV8y5YtvM7JobDHTA5HUZQLBoNBWv7d7Kf1awDCATTbuVyiW8ZgJkf0mqIoN7NqtYjIMRvUQ2RRDGZyOK6urikA6uXn4xittGwSjUbzjE2LI7IABjM5nA0bNlxRFCUBQOMNdhNFUY5v3rz5WxuWRmQRDGZySFVVVVsA1LTVaRYRKIoiIvJnuxRH1EEMZnJIO3fubAKwAkBTG5tFRI4mJSXttUNpRB3GYCaH5e7u/j6Aq+1sTrBxOUQW42LvAohu19GjR/VDhgy5IiK/AuCiKAoMBoNoNJrspKSk5+1dH9HtYo+ZHFpVVdUmRVEqjV8sURTFYDAYuJo1OTQGMzm0nTt36kXkdRFpbpk+9/3WrVsP2Lsuoo5gMJPDq66u3qgoSo1Go1EAcAiDHB6fIdAJbdiw4W53d/e1Go3G1d610I01NDS8/8QTT7xv7zrItvgHsxPSaDS+Li4uEwYOHNjV3rVQ+0pKSlBcXHwaAIO5k2Ewd1JarVYXHBxs7zLoBhoaGlBcXGzvMsgOOMZMRKQyDGYiIpVhMBMRqQyDmYhIZRjMREQqw2AmIlIZBjMRkcowmEl1srKysH79elRUVNi7FCK7YDCT6ixevBgvvvgiamtrLdpuU1MTdu/ejWvXrlm0XSJL4zf/qNMYO3Ys8vLyUFhYCHd3d3uXQ9Qu9pip06irq7N3CUQ3hcFMVnPy5ElMnDgRoaGhCAwMxLhx47B7926zfUQEb731FuLi4hAQEIBRo0bhwoULZvvs2rULI0aMQGBgIEJDQzF79mxkZWWZti9btgy+vr6YOXMmYmNjERgYiNGjR+ODDz4w7RMXF4eLFy8CAHr37g1fX1/s2LHDtD0jIwOzZ89GcHAwQkNDMWfOHGRkZJi2z507F76+vvjTn/6EkSNHwt/fH7NmzbLK50bEoQyyGi8vLxQVFaFfv364evUqMjIyMH/+fISEhGDQoEEAgBUrVmDz5s1wdXVFTEwMKisrUV1dbdZOU1MTdDodhg4digsXLuDLL79EVlYW0tPT4eHhYdrvxIkTmDx5MsrLy5GWloYlS5agubkZv/vd7zBp0iSkpKSgoaEB06ZNg6urK0JCQgAAP/zwAx588EE0NDRg8ODB0Ov1+OKLL/Dtt99i3759iI2NNb1HUlISxo0bh6ioKMycOdNmnyV1Luwxk9X06tULubm5+PTTT5GWlobExEQYDAbs2rULAJCbm4vNmzfDw8MDBw4cwKFDh5Ceno6IiAizdubMmYNjx45h9+7dOHbsGKZOnYqysjJ88803ZvslJiZiw4YN2LlzJzZt2gQAWLt2LQBg1apV8PHxAQCsW7cOycnJGDFiBADgueeeQ0NDA7Zu3YqDBw8iLS0Nb775Jurr6/G3v/3N7D1mzJiBf//733jnnXcwbdo0K3561Jmxx0xWYwy7HTt24Ny5c/hp5SegqKgIAHDgwE8rQM2cORMDBgwAALi4uFx3Y66kpARr165FWloaLl68CEX5aX2Hs2fPmu3n6vr/l/OsWbOwdOlSFBYWorKyEr6+vm3WeOHCBWRmZkKr1SI9PR3p6ekAYJq5cfz4cbP9Z8yY0cFPheiXMZjJaubNm4cDBw4gJCQE06dPR3l5OT7//HNcvXoVAFBaWgoACA0NbbeNmpoaTJw4ERcvXkRcXBzuvfdepKenIyMjA/X19e0epygKfHx8cOnSJdTU1LQbzMYadDod3n777eu2//wviW7dut3k2RPdPgYzWcXZs2dx4MABBAYG4r///S+6du2K7777Dp9//rmp53zXXXcBrcKxLZ988gkuXryIadOmISUlBQCwevVqZGRkmNppy7Vr10xfUPH29jbbZjAYTP/u5eUFAPD390dOTk6HzpnIUjjGTFZx5coVAEDPnj3RtetPK1gdPXoUAKDX6wEA/fr1A35a6RonT54EWmZpNDY2XteO8UZd63aam5vN3tN4nIhgzZo10Ov1iIyMNPWWu3fvDgA4c+YM0HJTMSIiAnfddRdKS0uxdetWU1vl5eXIz8+38KdCdHPYYyariIiIQI8ePXDixAn85je/QZcuXXDo0CGgJRhFBOPGjcPw4cPx/fffY+zYsYiMjERNTY3ZdLkRI0ZAURQkJSWhsLAQFy9exIkTJ0zttPb8888jJSUFpaWlprHo+Ph40/ahQ4ciLy8PDz30EMLDwxETE4N169YhISEBS5cuxfLly7FlyxZ4eXkhLy8PY8eOxXvvvWezz4zIiD1msgp3d3e89957GDJkCI4dO4aCggK8+eabmDNnDurq6kw95HfffRcPP/wwunXrhnPnziEqKgq9evUytTNw4ECsW7cOQUFB+OKLL6DRaLBz505ERkYiPT0dTU1Npn3Dw8Nx5swZVFVVYdiwYfjXv/6FqVOnmrYnJCRg4sSJaG5uxqlTp+Dn5wcA+O1vf4tt27Zh8ODBOH/+PHJycnD33XdjwoQJNv3MiIwUexdAtrdp06YhXl5eBydPnux9E7ur3rJly7Bt2zZs2rQJDz30kL3LsZhTp04hOzt73fz585+2dy1kW+wxExGpDIOZiEhlGMzk8N544w1UVlY61TAGdW4MZiIilWEwExGpDIOZiEhlGMxERCrDYCYiUhkGMzmdBQsWoKam5ob7NDc3w9/f32yVEiK1YDDTDZWXl+OZZ55BdHQ0goKCMGbMGKSmpt708aWlpZg7dy6Ki4stVtP27duve4C90Y8//ojU1FSzpafacubMGTQ3N6NPnz4dqsUa50fEYKZ2lZSU4P7770d2djbeeust7NmzB2PGjMGCBQuuWz2kPYcPH8bx48fNnn/RUevWrUNAQECb2xISEiAiyMzMvGEbJ0+eRO/evc2WprodHT2/1o8gJTJiMFO7nn32Wbi5uWHXrl2YMGECBg8ejMTERNxzzz345JNPgJanv/3jH/8wHfPPf/4TvXv3BgB89NFHWLJkCS5fvozg4GCsWLECADB69GisWLEC48ePR3BwMCZPnoy8vDwAQGpqKoKCgswCa8CAAdi4cSMAYOTIkSgoKEB8fDxCQkJQXl5u2m/37t3Izs7GxIkTr+sxnzlzBo888giCg4MxbNgwpKamIioqCgBw9epVrFixApGRkejVqxeGDh1qOr8b1dre+TU1NSExMRH9+vVDQEAAxo8fb3rW8/79+xESEoLVq1dj6NCheP31163wmyNHx2CmNp0+fRr79u3D8uXLTc9TNgoMDER5eTkaGxuRn59vtlhpdnY2+vbtCwCYPXs2Bg0ahPj4eJw/fx6rVq0CWnriFRUVeO+99/D111+jsbERL7zwAgAgJycHMTEx0Gh+ujRra2tx/vx503u88sor6NatG4qKinDu3Dn06NEDaAnDv/zlL1i8eDFGjBhh1mMuLi7GlClT4O3tjbS0NKxfvx5paWmIjo6GiODxxx9Hbm4uDh06hPz8fOh0OtOCsDeqtb3zmzdvHj777DNs374dp06dgr+/PzZs2AC09NTr6+sREhKCH374AU899ZTVfofkuBjM1KYjR45Ao9HggQceuG7buXPn4O/vj7y8POj1elMQoyVYjSGq0+mQmZmJuLg40/b6+npUVVXhueeeQ2BgIHr37o3Zs2fj1KlTpuN/3h4A02s//vgjBg0aZApuo61bt6Kurg5LlixBeHg48vLyoNPpgJYVT3r16oVNmzYhIiICw4cPx5133ono6Gikpqbi6NGjSE5ORkBAAPR6PYqLizF48OBfrLWt8/vyyy+xb98+rFu3DoMGDUJlZSUKCwsRHR1tOp8pU6aYvj7e0aEUck4MZmpTeXk5/Pz8rguO06dPo7CwEKNGjUJOTg78/Pzg7+9v2t46WDMzM6HT6UwLraJlZWw3NzeEh4ebXquqqjKtMtI62NHSAw8MDDRtP3bsmFkQGo9fvXo1nnvuOXTv3h0RERFoamoyDTmkpaVh2rRppkVcjb3wqKgofPzxx/j1r39tWkE7PT0d7u7uiIqK+sVa2zq/I0eOoFu3bpg1axbCwsIwfvx4TJkyBU888QTQ0mMeM2ZMB34z1BkwmKlN/v7+uHz5sul/6Y0SExMRGhqKSZMmITc319QTBICCggKUl5ebgvXYsWOIiIiAp6enaZ+cnBxERkbCxcUFaLn5tW/fPvzqV7/C1atXce7cObM2jx49ahbUx48fR//+/c1qWr16Nerq6vDKK68gLCwMU6ZMAVqCEy1hagxetKzO7erqioiICBQVFSEsLMy0be/evYiNjYWLi8sNa23v/NCyZFZ+fj4yMjKQn5+PP//5z9BoNGhubsbp06cRExNzW78T6jwYzNSmyZMno2vXrli8eDF+/PFHHD58GL///e9x6NAhbNmyBVqtFteuXUNlZSV0Oh3Kysrw9NNPw8XFxXRTraKiApcvX0ZRURHOnj0LtPQYtVotKioqcObMGSxevBi1tbV48skn0dTUBBHBpUuXgJa1AFNTU03B3NzcjKqqKuTk5KCkpAQ1NTUoKChAcnIytm7danqfs2fPIjg42HQDMCYmBh9//DFqamqQkZGBF198EXfffTe0Wi169eplWqLqhx9+wDvvvGNaCftGtbZ3fsYVW/bv3w+DwYBDhw7h2rVrAID8/Hw0Njaa/cVD1BYGM7WpR48e2LFjB8rKyjB9+nQsXLgQHh4e+OKLL0xDCXPnzsWVK1cQFRWFefPmoWfPnggPDzcNf8yYMQPu7u4YNmwYXn75ZaClx9zY2Ijhw4djwoQJaGxsxGeffQYfHx/4+PhgwYIFePLJJxEXF4fs7GxotVrT0IirqysWLlyIdevWYeTIkcjPz8fLL7+MAQMGYPr06Wb1h4eHm3rMa9asQX19PWJjY7Fy5UrExsaawjEhIQE5OTkYOHAgli9fjkmTJpmNd7dXa3vnN2XKFCxZsgTLli1DbGwsXn31Vbi5uQEtQe/v728aCiFqD5eW6oTsubRUVFQU3n77bYdYT8/etXJpqc6LPWaymYqKCpSVleGee+6xdym/yJFqJefDYCabycnJgYeHB4KCguxdyi9ypFrJ+bjauwDqPMaMGeMwz5RwpFrJ+bDHTESkMgxmIiKVYTATEakMg5mISGUYzEREKsNgJiJSGQYzEZHKcB5zJ1VfX+9+5MiRWnvXYSki4iIiiqIoekVRxN71WMKVK1fcRMQpzoVuDYO5EzIYDPkGg2FeSUmJvUuxmMLCwtdFJOjOO+9c7u3tfd7e9VhQrr0LINvjQ4zIKSxcuDBLUZS+er2+X3Jy8o2XyCZSOY4xExGpDIOZiEhlGMxERCrDYCYiUhkGMxGRyjCYySkoimKwdw1ElsJgJmfBqZ/kNBjMREQqw2AmZyEA4OLiwp4zOTwGMxGRyjCYiYhUhsFMTsH4FDaDdk3F5QAADBZJREFUwcChDHJ4DGYiIpVhMJNTMD6DmTf/yBkwmImIVIbBTESkMgxmcgq8+UfOhMFMRKQyDGYiIpVhMJNTMM7K4FAGOQMGMxGRyjCYyVlwHjM5DVd7F0Cdh4i4AuhijbaffvppjcFggJ+fn7uIdLXGewBoMA6ZEFkTexdkMyLyDIA37F1HB4QoinLe3kWQ8+NQBhGRyjCYiYhUhsFMDkmv1+PIkSMO2z4RkSqIyDNiIY8//rj07dvXUs3dbPvB9v4MqXNgj5kc0rVr1xy6fSIiVTD2mLdv3y7R0dHSpUsXCQ0NlcTERNHr9SIiotPpBIC8+uqrZl3VqVOnyvDhw0292ZZ5y6afwsJCERHx9vaW+++/X0aNGiUeHh4SHBws8fHxotPpLNE+e8xkE5zHTDaVkpKCefPm4bHHHkNiYiK+++47JCQkAABWrlx5U22sXLkSFy5cQEFBAbZv3w4ACAgIMG3Pzc3FmjVr0KtXL3z66ad49dVXUV1djfXr11ukfSIip6HX65/p1auXjB492qy3On/+fOnevbvU1dXdVI9WROThhx9uc4zZ29tbnn32WbPXFi1aJFqtVi5fvtzR9tljJpvgGDPZTHp6evfi4mLMnDnT7PVJkyahrq4Op06dssr7Tp48GTqdDsePH7dK+0SWxmAmm6mqquoCAD179jR73dfXFwBQXFxslff18fEBANTV1VmlfSJLYzCTzYSFhdUDwOXLl81ev3TpEtASoIpy808JaFm05BdduHABABAcHGyV9oksjcFMNhMREdEQFhaGzz77zOz1jz76CJ6enhg0aBBcXFzg6+uLixcvmraLCM6dO2d2jKenJ0pLS2EwGG74niKCd955Bz4+PoiOjrZ4+0REDk1EnklJSREAMn/+fNmxY4f84Q9/EADy0ksvme6wPfroo3LHHXdIamqqfPvttzJ79mzRarVmN+e2bdsmAGThwoWSkpIie/bsMd38CwoKkjVr1sjGjRtl/PjxAkD+/ve/W6J93vwjIudinMe8ceNG6dOnj2i1WgkLC5PXXntNDAaDKRQvXbokM2bMEC8vLwkODpZVq1bJAw88YBacer1eli5dKl5eXuLv7y8vvPCCKZiHDx8u/fv3Fzc3NwkPD5fXX3/dbGpFB9pnMBORc7HkV7Lb09Z0OQtiMJNNcIyZiEhlGMxERCrDr2STLVUCyLuF/V1vtvNQU1MT0Nzc7F5WVlbapUuXhps4pBnArU650N3i/kREzkVEdlhrsFhEHrL3+RG1h0MZREQqw2AmIlIZBjMRkcowmImIVIbBTA6nubkZbm5uSE9Pt3cpRFbBYCaHk5eXh+bmZkRFRQEAxo0bhz/+8Y+/eFxJSQkefPBBnD9/3gZVEt0+zmMmh5OVlYXw8HB4eHgALQ/aDwsL+8XjvvzySxw9ehTBwfxmNRHRbTHOY87Ly5OpU6eKp6en3HPPPTJjxgx58MEHRUQkPDxcAMgnn3ximqC8du1aCQ8Ply5dukj//v3FYDDI+++/L66urqLVasXT01OGDBnyH3ufHxGRwxGRHefPnxc/Pz957LHHJC8vT7755hvx9PSU+Ph4ERE5deqUAJCzZ8+KiEh8fLz07NlTdu/eLVVVVZKVlWUK7JEjR7Z+/Ce/YEJEdKtEZMeiRYtk0KBBZo8FDQsLkw8//FBERFJTU8XLy0tEREpKSkSr1ZqezdxaU1OTuLu7y1dffcVgJtXjzT9Stf3792PWrFmmJaFqa2tRVFSEvn37AgAyMzMRGxsLAPj666/h4uKCqVOnXtfOiRMnoNPpEBcXZ+MzILp1DGZStcrKStNirQDwn//8B66uroiMjAQAZGRkoF+/fkDLYqseHh7QaK6/rL///ntERkbC09PThtUT3R4GM6lav3798OGHH6K6uhrp6el4/vnn0adPH2i1WqClx2wM5qFDh6K6uhqrVq1CWVkZDh48iKKiIgBAWVkZysvLUVhYiIKCArueExGRwxKRHRkZGRIXFyeenp4yduxYmTp1qjz00EMiItLQ0CAuLi6tx41lw4YNEhISIm5ubtK/f38pLi4WEZGsrCwJCQkRrVYrc+bM4RgzEdHt4GM/qbPiUAYRkcowmImIVIZfySY1+wrAtZvZMS8vb3hVVdU9QUFB3wUFBZ2+iUPOdrw8IutQ7F0AkSUsXLjwbUVRFovIkqSkpA32roeoIziUQc7CAACKovCaJofHi5icgqIoxhWveU2Tw+NFTM6CwUxOgxcxOQURMbT8k9c0OTxexOQs2GMmp8GLmJwFb/6R0+BFTM5Cj5+GMlzsXQhRRzGYySkYx5jZYyZnwIuYnIJGo+EYMzkNXsTkLBjM5DR4EZNT4HQ5cia8iMlZsMdMToMPMSKHtGjRIrnZfUXkTFJSUh/rVkRkOexdkKNqFrlxNosIREQH4H9sVhWRBTCYyVEVAmj+hX0EQIWiKHtsVBORRTCYySEZDIY/thpXvk5Lb7pZo9Es37Jli86mxRF1EIOZHNLWrVs/E5FjIqJvZxcBUBIQEPBPG5dG1GEMZnJYiqK8LCKatsaaFUW5pijK6y+99FK7vWoitWIwk8NKSkr6HMD/Gp+T0ZqIXAWw2T6VEXUMg5kcmogsVRRF+dlrjQASOLZMjorBTA4tOTn5iIjsbZkWZ9RUXV2dYseyiDqEwUwOz2AwJABwwU+95QYAr+7cubPB3nUR3S4GMzm85OTkdBFJExFRFKWpurp6rb1rIuoIBjM5BV9f39d8fX0VAGvZWyZHx2AmpxAeHr5v1KhRtWPHjj1v71qIiDq9zZs36ysqKqSyslL27NlT8/7778+3d01ERJ1W61A2/jCciYjspK1QZjiTM+DzmMkhbd68WT9r1iyNRtP+bZJvvvmmtq6ubtljjz2WbNPiiDqIwUwO52ZC2YjhTI6IszLIodxKKAPAvffe69W9e/c3UlJSHrd6cUQWwmAmh3GroWx07733et1xxx1vMpzJUXAogxzC7YZya4cPH66qrq5+5vHHH+dzNEjVGMykepYIZSOGMzkCBjOpmiVD2YjhTGrHYCbVskYoGzGcSc0YzKRK1gxlI4YzqRWDmVTHFqFsxHAmNWIwk6rYMpSNGM6kNgxmUg17hLIRw5nUhMFMqmDPUDZiOJNaMJjJ7tQQykYMZ1IDBjPZlZpC2YjhTPbGYCa7UWMoGzGcyZ4YzGQXag5lI4Yz2Yt6/1SQ07JGKK9cuRITJ060WHsAMHr0aB8+lY7swcXeBVDnYulQ1ul0eOGFF9C7d2907doVBQUF6NKlC+666y6LtB8aGupRVlY2dsqUKaW7du36X4s0SvQLXO1dAHUe1ugpX7p0CZmZmdi7dy9cXFzg7u4OHx8f9OvXz2LvMXr0aJ/Dhw+/mZKSAg5rkC2wx0w2Ya0xZS8vL/j5+eHYsWMICQnB5MmTsWDBAou+B9hzJhvjGDNZnbVv9B09ehSPPPIIEhIScPDgQVy7ds0q78MxZ7IVzsogq7L17AsRgaJY97LmbA2yNvaYyWqSk5PfGjZsWIUtp8RZO5QBYNiwYT4ajWbpxo0be1r9zahTYo+ZrGrbtm2b+/TpMzs6OtrX3rVYQkNDg2Hv3r01CxYscIrzIXXizT+yql27dn06ZsyYcIPB0LtHjx4e9q6nIxjKZCsMZrI6ZwhnhjLZEoOZbMKRw5mhTLbGYCabccRwZiiTPTCYyaYcKZwZymQvDGayuVbh3KdHjx5u9q6nLQxlsicGM9nFrl27Pr3vvvv66PX6cLWFM0OZ7I3BTHaTmpq6R23hzFAmNWAwk12pKZwZyqQWDGayOzWEM0OZ1ITBTKpgz3BmKJPaMJhJNewRzgxlUiMGM6mKLcOZoUxqxWAm1bFFODOUSc0YzKRK1gxnhjKpHYOZVMsa4cxQJkfAYCZVs2Q4M5TJUTCYSfUsEc4MZXIkDGZyCB0J54aGBuzdu7eaoUyOgsFMDuN2wrmhoQH79u1jKJNDYTCTQ7mVcDaG8vz5831sVyFRxzGYyeHcTDgzlImI7GD79u3JR44cqamsrJTWP8XFxZKcnFxl7/qIiDqln4czQ5mISAWM4cxQJiJSkQ8//DBx+/btefaug4iIiIiIiIiIiIiIiIiIiIiIiIiIiIjIEf0fUQiGOFekuhYAAAAASUVORK5CYII=",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "ie_pipe.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Oo9Q8O0-_F-"
      },
      "source": [
        "### Define the sources and the extraction schema\n",
        "\n",
        "We select a list of URLs related to recent startup funding announcements.\n",
        "\n",
        "Additionally, we define a schema for the structured information we aim to extract."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-07-05T17:03:02.010606Z",
          "iopub.status.busy": "2024-07-05T17:03:02.010291Z",
          "iopub.status.idle": "2024-07-05T17:03:02.017510Z",
          "shell.execute_reply": "2024-07-05T17:03:02.016582Z",
          "shell.execute_reply.started": "2024-07-05T17:03:02.010578Z"
        },
        "id": "lkxnGFfR-_F-",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "urls = [\"https://techcrunch.com/2023/04/27/pinecone-drops-100m-investment-on-750m-valuation-as-vector-database-demand-grows/\",\n",
        "        \"https://techcrunch.com/2023/04/27/replit-funding-100m-generative-ai/\",\n",
        "        \"https://www.cnbc.com/2024/06/12/mistral-ai-raises-645-million-at-a-6-billion-valuation.html\",\n",
        "        \"https://techcrunch.com/2024/01/23/qdrant-open-source-vector-database/\",\n",
        "        \"https://www.intelcapital.com/anyscale-secures-100m-series-c-at-1b-valuation-to-radically-simplify-scaling-and-productionizing-ai-applications/\",\n",
        "        \"https://techcrunch.com/2023/04/28/openai-funding-valuation-chatgpt/\",\n",
        "        \"https://techcrunch.com/2024/03/27/amazon-doubles-down-on-anthropic-completing-its-planned-4b-investment/\",\n",
        "        \"https://techcrunch.com/2024/01/22/voice-cloning-startup-elevenlabs-lands-80m-achieves-unicorn-status/\",\n",
        "        \"https://techcrunch.com/2023/08/24/hugging-face-raises-235m-from-investors-including-salesforce-and-nvidia\",\n",
        "        \"https://www.prnewswire.com/news-releases/ai21-completes-208-million-oversubscribed-series-c-round-301994393.html\",\n",
        "        \"https://techcrunch.com/2023/03/15/adept-a-startup-training-ai-to-use-existing-software-and-apis-raises-350m/\",\n",
        "        \"https://www.cnbc.com/2023/03/23/characterai-valued-at-1-billion-after-150-million-round-from-a16z.html\"]\n",
        "\n",
        "\n",
        "schema={\n",
        "    \"Funding\": {\n",
        "        \"New funding\": \"\",\n",
        "        \"Investors\": [],\n",
        "    },\n",
        "     \"Company\": {\n",
        "        \"Name\": \"\",\n",
        "        \"Activity\": \"\",\n",
        "        \"Country\": \"\",\n",
        "        \"Total valuation\": \"\",\n",
        "        \"Total funding\": \"\"\n",
        "    }\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ym3t9xx9-_F-"
      },
      "source": [
        "### Run the Pipeline!\n",
        "\n",
        "We pass the required data to each component.\n",
        "\n",
        "Note that most of them receive data from previously executed components."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2024-07-05T17:03:02.019148Z",
          "iopub.status.busy": "2024-07-05T17:03:02.018790Z",
          "iopub.status.idle": "2024-07-05T17:05:23.850652Z",
          "shell.execute_reply": "2024-07-05T17:05:23.849624Z",
          "shell.execute_reply.started": "2024-07-05T17:03:02.019120Z"
        },
        "id": "UZxWu-CG-_F-",
        "outputId": "086f3fa3-20b4-4b6b-e04b-53164937ab4c",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            " 75%|███████▌  | 9/12 [01:53<00:41, 13.80s/it]You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "100%|██████████| 12/12 [02:32<00:00, 12.70s/it]\n"
          ]
        }
      ],
      "source": [
        "from tqdm import tqdm\n",
        "\n",
        "extracted_data=[]\n",
        "\n",
        "for url in tqdm(urls):\n",
        "    result = ie_pipe.run({\"fetcher\":{\"urls\":[url]},\n",
        "                          \"prompt_builder\": {\"schema\":schema}})\n",
        "\n",
        "    extracted_data.append(result[\"adapter\"][\"output\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-7e2T1Jt-_F-"
      },
      "source": [
        "Let's inspect some of the extracted data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2024-07-05T17:05:23.853030Z",
          "iopub.status.busy": "2024-07-05T17:05:23.852318Z",
          "iopub.status.idle": "2024-07-05T17:05:23.860399Z",
          "shell.execute_reply": "2024-07-05T17:05:23.859493Z",
          "shell.execute_reply.started": "2024-07-05T17:05:23.852987Z"
        },
        "id": "mNwz40Hc-_F-",
        "outputId": "f3814ef7-66cc-4c2e-f6f2-9050a6d66440",
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[{'Company': {'Activity': 'vector database',\n",
              "   'Country': '',\n",
              "   'Name': 'Pinecone',\n",
              "   'Total funding': '$138 million',\n",
              "   'Total valuation': '$750 million'},\n",
              "  'Funding': {'Investors': ['Andreessen Horowitz',\n",
              "    'ICONIQ Growth',\n",
              "    'Menlo Ventures',\n",
              "    'Wing Venture Capital'],\n",
              "   'New funding': '$100 million'}},\n",
              " {'Company': {'Activity': 'developing a code-generating AI-powered tool',\n",
              "   'Country': 'San Francisco',\n",
              "   'Name': 'Replit',\n",
              "   'Total funding': 'over $200 million',\n",
              "   'Total valuation': '$1.16 billion'},\n",
              "  'Funding': {'Investors': ['Andreessen Horowitz',\n",
              "    'Khosla Ventures',\n",
              "    'Coatue',\n",
              "    'SV Angel',\n",
              "    'Y Combinator',\n",
              "    'Bloomberg Beta',\n",
              "    'Naval Ravikant',\n",
              "    'ARK Ventures',\n",
              "    'Hamilton Helmer'],\n",
              "   'New funding': '$97.4 million'}}]"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "extracted_data[:2]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IGABuqOE-_F_"
      },
      "source": [
        "## Data exploration and visualization\n",
        "\n",
        "Let's explore the extracted data to assess its correctness and gain insights."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a4jxTrU5-_F_"
      },
      "source": [
        "### Dataframe\n",
        "\n",
        "We start by creating a Pandas Dataframe. For simplicity, we flatten the extracted data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-07-05T17:05:23.864364Z",
          "iopub.status.busy": "2024-07-05T17:05:23.864013Z",
          "iopub.status.idle": "2024-07-05T17:05:23.876818Z",
          "shell.execute_reply": "2024-07-05T17:05:23.876033Z",
          "shell.execute_reply.started": "2024-07-05T17:05:23.864335Z"
        },
        "id": "kraG_W1x-_F_",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "def flatten_dict(d, parent_key=''):\n",
        "    items = []\n",
        "    for k, v in d.items():\n",
        "        new_key = f\"{parent_key} - {k}\" if parent_key else k\n",
        "        if isinstance(v, dict):\n",
        "            items.extend(flatten_dict(v, new_key).items())\n",
        "        elif isinstance(v, list):\n",
        "            items.append((new_key, ', '.join(v)))\n",
        "        else:\n",
        "            items.append((new_key, v))\n",
        "    return dict(items)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-07-05T17:05:23.878524Z",
          "iopub.status.busy": "2024-07-05T17:05:23.878076Z",
          "iopub.status.idle": "2024-07-05T17:05:23.931907Z",
          "shell.execute_reply": "2024-07-05T17:05:23.930668Z",
          "shell.execute_reply.started": "2024-07-05T17:05:23.878473Z"
        },
        "id": "7JAQ8pO0-_F_",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.DataFrame([flatten_dict(el) for el in extracted_data])\n",
        "df = df.sort_values(by='Company - Name')\n",
        "\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![dataframe](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/haystack_dataframe.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DLDZvuZ0-_F_"
      },
      "source": [
        "Apart from some errors in \"Company - Country\", the extracted data looks good."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UXcXxat8-_F_"
      },
      "source": [
        "### Build a simple graph\n",
        "\n",
        "To understand the relationships between companies and investors, we construct a graph and visualize it."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kmu6akWJ-_F_"
      },
      "source": [
        "First, we build a graph using NetworkX.\n",
        "\n",
        "[NetworkX](https://networkx.org/) is a Python package that allows to create and manipulate networks/graphs in a simple way.\n",
        "\n",
        "Our simple graph will have companies and investors as nodes. We will connect investors to companies if they are mentioned in the same document."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-07-05T17:11:13.983879Z",
          "iopub.status.busy": "2024-07-05T17:11:13.982949Z",
          "iopub.status.idle": "2024-07-05T17:11:13.990146Z",
          "shell.execute_reply": "2024-07-05T17:11:13.989216Z",
          "shell.execute_reply.started": "2024-07-05T17:11:13.983845Z"
        },
        "id": "GEW3NPSg-_F_",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "import networkx as nx\n",
        "\n",
        "# Create a new graph\n",
        "G = nx.Graph()\n",
        "\n",
        "# Add nodes and edges\n",
        "for el in extracted_data:\n",
        "    company_name = el[\"Company\"][\"Name\"]\n",
        "    G.add_node(company_name, label=company_name, title=\"Company\")\n",
        "\n",
        "    investors = el[\"Funding\"][\"Investors\"]\n",
        "    for investor in investors:\n",
        "        if not G.has_node(investor):\n",
        "            G.add_node(investor, label=investor, title=\"Investor\", color=\"red\")\n",
        "        G.add_edge(company_name, investor)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "67mdKYMs-_F_"
      },
      "source": [
        "Next, we use Pyvis to visualize the graph.\n",
        "\n",
        "[Pyvis](https://pyvis.readthedocs.io/en/latest/) is a Python package for interactive visualization of networks/graphs. It integrates nicely with NetworkX."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "execution": {
          "iopub.execute_input": "2024-07-05T17:20:13.616822Z",
          "iopub.status.busy": "2024-07-05T17:20:13.616413Z",
          "iopub.status.idle": "2024-07-05T17:20:13.888649Z",
          "shell.execute_reply": "2024-07-05T17:20:13.887648Z",
          "shell.execute_reply.started": "2024-07-05T17:20:13.616789Z"
        },
        "id": "vEtXBviT-_GA",
        "outputId": "77f13466-b806-4653-d514-6e2c58f3de0f",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from pyvis.network import Network\n",
        "from IPython.display import display, HTML\n",
        "\n",
        "\n",
        "net = Network(notebook=True, cdn_resources='in_line')\n",
        "net.from_nx(G)\n",
        "\n",
        "net.show('simple_graph.html')\n",
        "display(HTML('simple_graph.html'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![graph visualization](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/haystack_graph.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "76celtVxE4Cw"
      },
      "source": [
        "Looks like Andreessen Horowitz is quite present in the selected funding announcements 😊"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oFH6YjsV-_GA"
      },
      "source": [
        "## Conclusion and ideas\n",
        "\n",
        "In this notebook, we demonstrated how to set up an information extraction system using a small language model (NuExtract) and Haystack, a customizable orchestration framework for LLM applications.\n",
        "\n",
        "How can we use the extracted data?\n",
        "\n",
        "Some ideas:\n",
        "-  The extracted data can be added to the original documents stored in a [Document Store](https://docs.haystack.deepset.ai/docs/document-store?utm_campaign=developer-relations&utm_source=hf-cookbook). This allows for advanced search capabilities with [metadata filtering](https://docs.haystack.deepset.ai/docs/metadata-filtering?utm_campaign=developer-relations&utm_source=hf-cookbook).\n",
        "- Expanding on the previous idea, you can do RAG (Retrieval Agumented Extraction) with metadata extraction from the query, as explained in [this blog post](https://haystack.deepset.ai/blog/extracting-metadata-filter?utm_campaign=developer-relations&utm_source=hf-cookbook).\n",
        "- Store the documents and extracted data in a Knowledge Graph and perform Graph RAG ([Neo4j-Haystack integration](https://prosto.github.io/neo4j-haystack))."
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
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    "kaggle": {
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      "dataSources": [],
      "dockerImageVersionId": 30732,
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